Medical Image Analysis of Gastric Cancer in
Digital Histopathology: Methods, A pplications
and Challenges
v or gelegt v on
Master of T echnology
Harshita SHARMA
geb . in Neu Delhi, Indien
v on der F akultät IV -Elektrotechnik und Informatik
der T echnischen Uni versität Berlin
zur Erlangung des akademischen Grades
Doktorin der Ingenieurwissenschaften
- Dr .-Ing -
genehmigte Dissertation
Pr omotionsausschuss :
V orsitzender: Prof. Dr .-Ing. Thomas SIK ORA
Gutachter: Prof. Dr .-Ing. Olaf HELL WICH
Prof. Dr .-Ing. Niels GRABE
Prof. Dr . rer . nat. Peter HUFN A GL
T ag der wissenschaftlichen Aussprache: 25. April 2017
Berlin 2017

Dedicated to my family and lo ved ones.
“I have become my own version of an optimist. If I can’ t make it thr ough one
door , I’ll go thr ough another door – or I’ll make a door . Something terrific will
come no matter how dark the pr esent. ”
— Rabindranath T agor e

Ackno wledgements
“At times our own light goes out and is r ekindled by a spark fr om another person. Eac h of
us has cause to think with deep gr atitude of those who have lighted the flame within us. ”
— Albert Schweitzer
The undertaking and completion of this dissertation has prov ed to be an e ventful journe y ,
filled with trib ulations and challenges, both professional and personal. W ith proper coordination
and guidance I was able to o v ercome the hurdles and realize my goals. Hence, it is with immense
gratitude that I ackno wledge those, who ha v e pro vided me with inspiration, financial and moral
support, and technical direction.
There are no words which can adequately e xpress my gratitude for the in v aluable guidance
and encouragement, which I ha v e been extremely fortunate to recei ve from my supervisor
Pr of.
Dr . -Ing . Olaf Hellwich
, Professor and Head, Computer V ision and Remote Sensing Group,
Department of Computer Engineering and Microelectronics, T echnical Univ ersity Berlin, Berlin,
German y . He kindly welcomed me in his group, and guided me throughout the course of my
in vestig ation. His generous support and stimulating suggestions ha ve been the dri ving force for
me throughout my work. Besides being an e xcellent guide, he has been conferring v aluable hints
to impro ve v arious aspects of my research. It was the inspiration pro vided by him that ga v e me
the confidence to pursue this inno v ati v e topic and work it through to a successful completion.
I am greatly indebted and e xpress my sincere gratitude to
Pr of. Dr . r er . nat. Peter Hufnagl
,
Professor and Head, Department of Digital Pathology and IT , Institute of Pathology “Rudolf-
V ircho w-House”, Charité Uni versity Hospital, Berlin, Germany . He granted the appro v al to
conduct my research in collaboration with the group at Charité Uni v ersity Hospital, and provided
me necessary resources for the same. He of fered me additional of fice space to perform my work
at Charité Uni v ersity Hospital, and has been really welcoming, encouraging and supporti v e
to w ards my ideas. His in-depth domain kno wledge and consistent moti v ation hav e been most
essential for a deeper understanding of the problem and deciding the next course of action.
Furthermore, he advised me to utilize parts of his doctorate research comprising of the object-
le v el shape-based feature e xtraction for my studies, which prov ed an important ingredient for the
success of my work.

iv
I sincerely thank
Dipl-Inf . Norman Zerbe
, Project Manager , Department of Digital Pathol-
ogy and IT , Institute of Pathology “Rudolf-V ircho w-House”, Charité Uni versity Hospital, Berlin,
German y for his extremely useful suggestions during my research. His wide kno wledge and
logical way of thinking ha ve been truly inspiring. He has alw ays been forthcoming to pro vide his
assistance during v arious steps of my research. I also thank him for his semi-automatic whole
slide image re gistration implementation, and for providing a subset of cell nuclei shape features.
I am immensely thankful to
Pr of. Dr . med. Christoph Röcken
, Director , Department of
P athology , Uni versity Medical Center Schleswig Holstein (UKSH), Campus Kiel, Kiel, German y
for collaboration with Charité, and allo wing me to acquire the necessary gastric cancer whole
slide images used in my research. A former PhD candidate under his supervision,
Dr . rer .
medic. Hans-Michael Behrens
, has been in direct contact with the research group at Charité,
assisted me during the data acquisition stage and pro vided me with the associated kno wledge, to
whom I am e xtremely thankful.
I am grateful to
Dr . med. Christine Böger
, P athologist, Uni versity Medical Center
Schleswig Holstein (UKSH), Campus Kiel, Kiel, German y , for gi ving me her precious time by
performing the re vie w of ground truth data for the cell nuclei classification step. I sincerely thank
Dr . med. Iris Klempert
, P athologist, Institute of P athology , Charité Uni v ersity Hospital, Berlin,
German y for creating the ground truth for the necrosis detection step. I am also thankful to
PD
Dr . med. Fr edrick Klauschen
, P athologist, Institute of Pathology , Charité Uni versity Hospital,
Berlin, German y for introducing me to the histological architecture and v arious cell nuclei types
in our datasets, and also for visually identifying necrosis in the tissue.
I truly admire the support and assistance from all the members of Department of Digital
P athology and IT , Institute of P athology , Charité Uni v ersity Hospital, Berlin, Germany . Es-
pecially , I would like to thank
Dr . rer . medic. Stephan W ienert
, Postdoctoral Scientist, for
pro viding his implementation of cell nuclei segmentation as required for our H&E stained
gastric cancer images, and to introduce me to the functionality of CognitionMaster softw are. I
thank
M.Sc. Björ n Lindequist
, Scientist, for his contrib ution in the preparatory stage and his
implementation of four object-le v el gray le v el co-occurrence matrix texture-based features. I
also thank
M.Sc. Daniel Heim
, Scientist, for his optimized version of AdaBoost algorithm as
required for my work. I am e xtremely thankful to
M.Sc. Sebastian Lohmann
, Scientist, to
introduce me to certain te xture-based approaches such as Gabor filter -banks, and also to a fe w
graph-based state-of-the-art approaches. I am grateful to
VMscope GmbH
, Berlin, Germany ,
for pro viding me indi vidual licenses of two of their softw are tools, namely V irtual Slide Access
SDK 4.0 and VM Slide Explor er 2010 , in order to access whole slide image data, as required for
this work.
My sincere thanks goes to all the members of Remote Sensing and Computer V ision Group,
Department of Computer Engineering and Microelectronics, T echnical Uni versity , Berlin, Berlin
German y . I would specially lik e to thank
Dr .-Ing. Ronny Hänsch
, Postdoctoral Scientist,
for constructi v e feedback and comments about my work, especially for the presentations and
dissertation. Furthermore, my sincere thanks goes to
Dipl.-Ing . W olfgang Stinner
, our technical

v
colleague who pro vided hardware or software assistance at the of fice, that helped me to perform
the requisite computer -based tasks uninterruptedly in the duration of my studies.
I am indebted and filled with gratitude to wards
D AAD (Deutscher Akademischer A us-
tausch Dienst: German Academic Exchange Service)
for gi ving me this wonderful opportu-
nity to conduct my PhD studies in Berlin, Germany , and providing the requisite financial support
for the same.
I wish to thank my family for the inspirational impetus and moral support during the course
of work. A big thanks to my parents and sister , for the unconditional af fection and care I recei v ed
from them in my good and bad times. My special thanks to Rahul who encouraged and supported
me throughout this journey . I also w ant to express gratitude to wards my friends and relati v es
who helped directly or indirectly for the completion of this work. Last but not the least, I w ould
like to e xpress my deepest gratitude to the Almighty for sho wering blessings on me.
Thank you. Dank e. Gracias. Grazie. Merci. Shukriya.

Medical Image Analysis of Gastric Cancer in Digital Histopathology: Methods,
A pplications and Challenges
Abstract:
Medical image analysis in digital histopathology is a currently expanding and e xciting field of
scientific research. In this work, histopathological image analysis is e xtensi vely studied and a systematic
frame work for computer -based analysis in H&E stained whole slide images of gastric car cinoma is proposed.
The exhausti ve e xperimental study comprises of three fundamental stages, namely , pr eparation of materials ,
imag e pr e-analysis and analysis of cancer r e gions . These stages collecti v ely incorporate the understanding,
formulation, implementation and e v aluation of suitable image analysis tasks required to achie v e the defined
research objecti ves in each stage, for e xample, re gistration and annotation transformation between whole
slide images, cell nuclei segmentation and classification, multiresolution combination of visual information
for segmentation enhancement, appearance-based necrosis detection, and cancer classification based on
immunohistochemistry . Computerized applications are also demonstrated as an outcome of the conducted
research, including computer -aided diagnosis, content-based image retrie v al and automatic determination of
tissue composition.
The research focuses on the de v elopment of methods for ef fecti ve representation and subsequent
classification of regions of interest in histopathological image datasets. For this purpose, two image analysis
routes, namely , traditional route and deep learning route are in v estigated. The traditional route consists of
handcrafted feature extraction to describe te xtural, color , intensity , morphological and architectural properties
of tissues follo wed by traditional machine learning methods including support v ector machines, AdaBoost
ensemble learning and random forests. In this domain, graph-based methods are extensi v ely explored due to
their ability to suitably represent the spatial arrangements and neighborhood relationships in tissue regions. A
nov el graph-based image description method called cell nuclei attrib uted r elational graph is proposed along
with multiple v ariants, for kno wledge description of indi vidual component characteristics, spatial interactions
and underlying tissue architecture in histopathological images. In the deep learning route, con v olutional
neural networks are thoroughly in vestig ated. A self-designed con volutional neur al network ar chitectur e is
introduced and analyzed for cancer classification and necrosis detection, also compared with a widely-kno wn
frame work and ensemble of deep netw orks.
During the detailed in vestigation, system performance is rigorously analyzed using dif ferent experimental
aspects, for instance, algorithm parameters, feature configurations and classification strategies. The entire
proposed frame work is quantitati vely e v aluated at each stage using a set of performance metrics. A reasonable
performance is achie ved usi ng the described methods, comparing fa vorably , and e ven outperforming the
state-of-the-art techniques in certain occasions. The empirical observ ations, corresponding conclusions,
scientific implications and future directions of the research are thoroughly discussed.
Collecti vely , the discussed histopathological image analysis methods in H&E stained gastric cancer
whole slide images aim to reduce manual preparation ef forts, inspection times, and inter -and intra-observer
v ariability . Moreov er , the de veloped methods can potentially impro v e the current state of technology through
applications such as automatic classification, content-based image retrie v al, archi ving, bio-banking, mark er
quantification, detecting malignant changes ov er time, pro viding second opinions to pathologists, thereby
contrib uting to wards diagnosis, prognosis, education and research in biology and medicine.
K eywords:
Digital histopathology , gastric carcinoma, whole slide images, cancer classification, necrosis
detection, handcrafted feature e xtraction, attrib uted relational graphs, traditional machine learning, deep
learning, con volutional neural netw orks, computer-aided diagnosis.

Medizinische Bildanalyse von Magenkarzinom in der Digitalen Histopathologie:
Methoden, Anwendungen und Herausf orderungen
Zussamenfassung:
Medizinische Bildanalyse in der digitalen Histopathologie ist ein derzeit expandieren-
des und spannendes Forschungsfeld. In dieser Arbeit wird die histopathologische Bildanalyse umfassend
untersucht und ein systematischer Ansatz für computerbasierte Analysen H&E-gefärbter virtueller Schnitte
v on Magenkarzinom v orgeschlagen. Die erschöpfende experimentelle Studie besteht aus drei grundle genden
Stadien, nämlich V orber eitung von Materialien , Bildvor analyse und Analyse von Kr ebsr e gionen . Diese
Phasen umfassen das V erständnis, die Formulierung, die Implementierung und die Be wertung v on geeigneten
Bildanalyseaufgaben, die erforderlich sind, um die definierten F orschungsziele in jedem Stadium zu erre-
ichen, beispielsweise die Registrierung und die Annotationstransformation zwischen virtuellen Schnitten,
Zellkernse gmentierung und Klassifikation, K ombination visueller Informationen unterschiedlicher Auflösung
zur Segmentierungsv erbesserung, aussehensbasierte Nekrose-Erkennung, und Krebs-Klassifizierung auf
der Grundlage der Immunhistochemie. Als Ergebnis werden computer gestützte Anwendungen der durchge-
führten Forschung gezeigt, einschließlich computer gestützter Diagnose, inhaltsbasierter Bildwieder ge win-
nung und automatischer Bestimmung der Ge webezusammensetzung.
Die durchgeführte Forschung betrif ft die Entwicklung von Methoden für eine ef fekti v e Darstellung
und anschließende Klassifizierung in histopathologischen Bilddatensätzen. Dazu werden zwei Bildanal-
yse wege untersucht, nämlich ein traditioneller und ein Deep Learning Ansatz. Der traditionelle Ansatz
besteht aus einer Merkmalsextraktion, um T e xtur , Farbe, Intensität, morphologische und architektonis-
che Eigenschaften v on Ge weben zu beschreiben, gefolgt von traditionellen maschinellen Lernmethoden,
einschließlich der Support V ector Maschinen, AdaBoost Ensemble Learning und Random Forests. Auf
diesem Gebeit werden graphenbasierte V erf ahren aufgrund ihrer Fähigkeit, die räumlichen Anordnungen
und Nachbarschaftsbeziehungen in Ge webebereichen geeignet darzustellen, umfassend erforscht. Ein neues
graphisches Bildbeschreib ungsverf ahren namens Cell-nuclei Attrib uted Relational Graph wird zusammen
mit mehrere V arianten zur W issensbeschreib ung einzelner K omponentenmerkmale, räumlicher Interaktionen
und zugrunde liegenden Ge webearchitekturen in histopathologischen Bildern vor geschlagen. Im Deep
Learning Bereich werden Con volutional Neural Netw orks gründlich untersucht. Eine Con volutional Neur al
Network Ar chitektur wird eingeführt und für die Krebsanalyse und Nekrose-Detektion verwendet, und mit
einer weithin bekannten Netzwerk Architektur und Ensembln v on Deep Netzwerkn ver gleichen.
Während der Detailuntersuchungen wurde die Systemleistung unter unterschiedlichen Aspekten wie
Algorithmenparametern, Merkmalskonfigurationen und Klassifizierungsstrate gien analysiert. Der vor geschla-
gene Ansatz wird in jedem Stadium quantitati v unter V erwendung eines Satzes v on Leistungsmetriken
be wertet. Mit den entwickelten Methoden wird eine Leistung erzielt, die den Stand der T echnik erreicht oder
besser ist. Die empirischen Beobachtungen, die entsprechenden Schlussfolgerungen, die wissenschaftlichen
Implikationen und die zukünftigen Forschungsrichtungen werden eingehend erörtert.
Zusammenfassend kann festgestellt werden, dass die diskutierten histopathologischen Bildanalyse v-
erfahren in H& E-gefärbten virtuellen Schnitten v on Magenkrebs manuelle V orbereitung, Prüfzeiten und
Er gebnisv ariabilität reduzieren. Darüber hinaus können die entwickelten Methoden den aktuellen Stand der
T echnik durch Anwendungen, wie beispielsweise automatische Klassifizierung, inhaltsbasierte Bildsuche,
Archi vierung, Bio-Banking und Marker -Quantifizierung v erbessern. Bei der Analyse, bösartiger V eränderun-
gen im Zeitverlauf bieten sie eine zweite Meinung zu der der P athologen, und dadurch einen Beitrag zur
Diagnose und Prognose in Biologie und Medizin.
Schlüsselwörter:
Digitale Histopathologie, Magenkarzinom, V irtuelle Schnitte, Krebs-Klassifikation,
Nekrose-Erkennung, Merkmalse xtraktion, Attrib utierte Relationale Graphen, Maschinelles Lernen, Deep
Learning, Con volutional Neural Netw orks, computergestützte Diagnose.

Contents
List of Figur es xvii
List of T ables xxvii
List of Abbr e viations xxxi
1 Intr oduction 1
1 . 1 B a c k g r o u n d .................................... 1
1 . 2 S t u d y O b j e c t i v e s ................................. 2
1 . 3 M o t i v a t i o n s .................................... 3
1 . 4 C o n t r i b u t i o n s ................................... 5
1.4.1 Scientific Contrib utions . . . . . . . . . . . . . . . . . . . . . . . . . 5
1 . 4 . 2 A p p l i c a t i o n A r e a s ............................ 6
1 . 5 C h a l l e n g e s i n S t u d y ................................ 7
1 . 6 O r g a n i z a t i o n o f T h e s i s .............................. 8
2 Theor etical Backgr ound 9
2 . 1 I n t r o d u c t i o n.................................... 9
2 . 2 D o m a i n D e s c r i p t i o n ................................ 10
2.2.1 Overvie w of Digital Histopathology . . . . . . . . . . . . . . . . . . . 10
2.2.2 Introduction to Gastric Histopathology . . . . . . . . . . . . . . . . . 12
2.3 Feature Extraction in Digital Histopathology . . . . . . . . . . . . . . . . . . . 14
2.3.1 Lo w-le v el (Pixel-based) Methods . . . . . . . . . . . . . . . . . . . . 15
2 . 3 . 2 O b j e c t - l e v e l M e t h o d s ........................... 21
2.3.3 High-le v el (Architectural) Methods . . . . . . . . . . . . . . . . . . . 22
2.4 Machine Learning in Digital Histopathology . . . . . . . . . . . . . . . . . . . 33
2.4.1 Support V ector Machines . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.4.2 AdaBoost Ensemble Learning . . . . . . . . . . . . . . . . . . . . . . 34
2 . 4 . 3 R a n d o m F o r e s t s ............................. 34
2.4.4 Deep Con v olutional Neural Networks . . . . . . . . . . . . . . . . . . 35
2 . 5 S u m m a r y ..................................... 36

xii Contents
3 Related W ork in Digital Histopathology 37
3 . 1 I n t r o d u c t i o n.................................... 37
3.2 Feature Extraction in Digital Histopathology . . . . . . . . . . . . . . . . . . . 38
3.2.1 Lo w-le v el (Pixel-based) Methods . . . . . . . . . . . . . . . . . . . . 38
3.2.2 Object-le v el Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2.3 High-le v el (Architectural) Methods . . . . . . . . . . . . . . . . . . . 40
3.3 Machine Learning in Digital Histopathology . . . . . . . . . . . . . . . . . . . 43
3.3.1 Support V ector Machines . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3.2 AdaBoost Ensemble Learning . . . . . . . . . . . . . . . . . . . . . . 44
3 . 3 . 3 R a n d o m F o r e s t s ............................. 44
3.3.4 Deep Con v olutional Neural Networks . . . . . . . . . . . . . . . . . . 44
3.4 Image Analysis in Gastric Cancer . . . . . . . . . . . . . . . . . . . . . . . . 45
3 . 5 S u m m a r y ..................................... 46
4 Over view of Resear ch Methodology 47
4 . 1 I n t r o d u c t i o n.................................... 47
4.2 Stage 1: Preparation of Materials . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.3 Stage 2: Image Pre-analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4 Stage 3: Analysis of Cancer Regions . . . . . . . . . . . . . . . . . . . . . . . 49
4 . 5 S u m m a r y ..................................... 50
5 Stage 1: Pr eparation of Materials 51
5 . 1 I n t r o d u c t i o n.................................... 51
5.2 Whole Slide Image Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5 . 2 . 1 S p e c i m e n P r o p e r t i e s ........................... 52
5 . 2 . 2 S c a n n e r D e t a i l s .............................. 52
5.3 Annotations: HER2 whole slide images . . . . . . . . . . . . . . . . . . . . . 54
5 . 3 . 1 L a b e l i n g P r o c e s s ............................. 54
5.3.2 Semi-automatic WSI Registration . . . . . . . . . . . . . . . . . . . . 55
5.3.3 Annotation T ransformation . . . . . . . . . . . . . . . . . . . . . . . . 57
5 . 4 I n i t i a l W o r k i n g D a t a s e t s ............................. 59
5.4.1 Annotations for Cell Nuclei Segmentation Ev aluation . . . . . . . . . . 60
5.4.2 Annotations for Cell Nuclei Classification . . . . . . . . . . . . . . . . 60
5.5 Annotations for Necrosis Detection . . . . . . . . . . . . . . . . . . . . . . . . 64
5.5.1 Datasets for SVM-based Method . . . . . . . . . . . . . . . . . . . . . 66
5.5.2 Datasets for Deep Learning Methods . . . . . . . . . . . . . . . . . . 66
5 . 6 S u m m a r y ..................................... 66
6 Stage 2: Image Pr e-analysis 69
6 . 1 I n t r o d u c t i o n.................................... 69
6 . 2 N e c r o s i s D e t e c t i o n ................................ 70

Contents xiii
6 . 2 . 1 B a c k g r o u n d ................................ 70
6 . 2 . 2 M o t i v a t i o n ................................ 71
6 . 2 . 3 F e a t u r e E x t r a c t i o n ............................ 72
6 . 2 . 4 M a c h i n e L e a r n i n g ............................ 73
6.3 Cell Nuclei Segmentation and Ev aluation . . . . . . . . . . . . . . . . . . . . 79
6.3.1 Segmentation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 79
6.3.2 Segmentation Ev aluation . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.4 Multiresolution Segmentation Enhancement . . . . . . . . . . . . . . . . . . . 84
6 . 4 . 1 B a c k g r o u n d ................................ 84
6.4.2 Cell Nuclei Classification . . . . . . . . . . . . . . . . . . . . . . . . 85
6.4.3 Multiresolution Combination . . . . . . . . . . . . . . . . . . . . . . . 93
6 . 5 A p p l i c a t i o n s .................................... 95
6.5.1 Automatic Necrosis Detection . . . . . . . . . . . . . . . . . . . . . . 95
6.5.2 Automatic Determination of Histological T issue Composition . . . . . 96
6 . 6 S u m m a r y ..................................... 98
7 Stage 3: Analysis of Cancer Regions 99
7 . 1 I n t r o d u c t i o n.................................... 100
7.2 Ground T ruth Dataset Generation . . . . . . . . . . . . . . . . . . . . . . . . . 101
7.2.1 Datasets for T raditional Machine Learning Methods . . . . . . . . . . 101
7.2.2 Datasets for Deep Learning Methods . . . . . . . . . . . . . . . . . . 102
7.3 Image Description Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
7.3.1 Lo w-le v el State-of-the-art Features . . . . . . . . . . . . . . . . . . . 105
7.3.2 High-le v el State-of-the-art Features . . . . . . . . . . . . . . . . . . . 107
7.3.3 High-le v el Handcrafted Features . . . . . . . . . . . . . . . . . . . . . 108
7.3.4 Combination of Features . . . . . . . . . . . . . . . . . . . . . . . . . 121
7.4 Machine Learning Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
7 . 4 . 1 T r a d i t i o n a l M e t h o d s ........................... 123
7.4.2 Deep Con v olutional Neural Networks . . . . . . . . . . . . . . . . . . 125
7.4.3 Classification Strate gies . . . . . . . . . . . . . . . . . . . . . . . . . 135
7 . 5 A p p l i c a t i o n s .................................... 136
7.5.1
Computer -aided Diagnosis: Cancer Classification based on Immunohis-
t o c h e m i s t r y ................................ 136
7.5.2 Content-based Image Retriev al . . . . . . . . . . . . . . . . . . . . . . 137
7 . 6 S u m m a r y ..................................... 141
8 P erf ormance Ev aluation 143
8 . 1 I n t r o d u c t i o n.................................... 143
8 . 1 . 1 E v a l u a t i o n M e t h o d s ........................... 144
8 . 1 . 2 P e r f o r m a n c e M e t r i c s ........................... 146
8.2 Comparati v e Evaluation: Necrosis Detection . . . . . . . . . . . . . . . . . . . 149

xiv Contents
8.3 Comparati v e Evaluation: Cell Nuclei Classification . . . . . . . . . . . . . . . 151
8.4 Comparati v e Evaluation: Analysis of Cancer Regions . . . . . . . . . . . . . . 153
8.4.1 Concept of Comparativ e Ev aluation . . . . . . . . . . . . . . . . . . . 153
8.4.2 Lo w-le v el State-of-the-art Features . . . . . . . . . . . . . . . . . . . 157
8.4.3 High-le v el State-of-the-art Features . . . . . . . . . . . . . . . . . . . 163
8.4.4 High-le v el Handcrafted Features . . . . . . . . . . . . . . . . . . . . . 164
8.4.5 Combination of Features . . . . . . . . . . . . . . . . . . . . . . . . . 173
8.4.6 Deep Learning Methods . . . . . . . . . . . . . . . . . . . . . . . . . 175
8.4.7 Overall Observ ations . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
8 . 5 S u m m a r y ..................................... 182
9 Statistical Analysis of Stains: Observ ations and Challenges 183
9 . 1 I n t r o d u c t i o n.................................... 183
9.2 V isualizing Image Statistics: HER2 Stain . . . . . . . . . . . . . . . . . . . . 184
9 . 2 . 1 V i s u a l I n s p e c t i o n ............................. 184
9 . 2 . 2 F e a t u r e H e a t m a p s ............................. 185
9.3 V isualizing Image Statistics: H&E Stain . . . . . . . . . . . . . . . . . . . . . 186
9 . 3 . 1 V i s u a l I n s p e c t i o n ............................. 186
9 . 3 . 2 F e a t u r e H e a t m a p s ............................. 187
9 . 4 S u m m a r y ..................................... 189
10 Summary 191
1 0 . 1 C o n c l u s i o n s .................................... 191
10.2 Future W ork and Recommendations . . . . . . . . . . . . . . . . . . . . . . . 195
1 0 . 2 . 1 F u t u r e W o r k ............................... 195
1 0 . 2 . 2 R e c o m m e n d a t i o n s ............................ 198
A ppendix A Implementation Details 201
A . 1 H a r d w a r e S p e c i fi c a t i o n s ............................. 201
A . 2 O p e r a t i n g S y s t e m s ................................ 202
A . 3 P r o g r a m m i n g L a n g u a g e s ............................. 202
A . 4 S u p p o r t i n g S o f t w a r e ............................... 203
A.5 Computational Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
A.6 Examples of Metadata Screenshots . . . . . . . . . . . . . . . . . . . . . . . . 212
A ppendix B Detailed Experimental Results 215
B . 1 C o n f u s i o n M a t r i c e s ................................ 215
B.1.1 Comparati v e Evaluation: Necrosis Detection . . . . . . . . . . . . . . 215
B.1.2 Comparati v e Evaluation: Cell Nuclei Classification . . . . . . . . . . . 216
B.1.3 Comparati v e Evaluation: Analysis of Cancer Regions . . . . . . . . . . 218
B.2 Box and Whisker Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224

Contents xv
B.2.1 Comparati v e Evaluation: Necrosis Detection . . . . . . . . . . . . . . 224
B.2.2 Comparati v e Evaluation: Cell Nuclei Classification . . . . . . . . . . . 225
B.2.3 Comparati v e Evaluation: Analysis of Cancer Regions . . . . . . . . . . 226
Bibliograph y 238

List of Figur es
2.1 A schematic ov ervie w of tissue specimen preparation . . . . . . . . . . . . . . 10
2.2 Pipeline of virtual microscopy . Adapted from [ Saeger 2009 ] . .......... 11
2.3 Pictorial representation of the stomach. Source: [ W ikipedia 2017 ] ....... 13
2.4 Example of Gabor filter -bank with v arying frequencies and orientations . . . . 17
2.5 The LBP code generated from an example image . . . . . . . . . . . . . . . . 18
2.6
The RFS filter -bank to obtain MR8 filter responses in the V arma-Zisserman
approach. Constructed using the open-source MA TLAB filter implementation
in [ V arma 2002 ] .................................. 19
2.7
Example sho wing (a) an image (b) gray model and image gray histogram (c)
RGB color model and image component histograms (d) HSV color model and
image component histograms (generated in MA TLAB) . . . . . . . . . . . . . 20
2.8 V oronoi Diagram of a set of random points . . . . . . . . . . . . . . . . . . . . 23
2.9
Delaunay T riangulation corresponding to the V oronoi diagram in Figure 2.8
sho wing empty circle and duality properties . . . . . . . . . . . . . . . . . . . 24
2.10
Gabriel Graph corresponding to Delaunay graph in Figure 2.9 sho wing empty
c i r c l e p r o p e r t y ................................... 24
2.11
Relati v e Neighborhood Graph corresponding to Delaunay graph in Figure 2.9
s h o w i n g e m p t y l u n e p r o p e r t y ........................... 25
2.12
Euclidean minimum spanning tree corresponding to Delaunay graph in Figure 2.9
25
2.13 Nearest Neighbor Graph corresponding to Delaunay graph in Figure 2.9 .... 26
2.14 A V oronoi diagram and its corresponding Ulam T ree . . . . . . . . . . . . . . 27
2.15 β
neighborhoods (shaded re gions) with dif ferent v alues of
β
.(a) Lune-based and
circle-based
β = 0 . 5
(b) Lune-based and circle-based
β = 1
(c) Lune-based
β = 2 (d) Circle-based β = 2 ........................... 28
2.16
Johnson-Mehl tessellation for a set of random points (a) gro wth of particles at
t = t 1 (b) gro wth of particles at t = t 1 + 50 ................... 29
2.17 O’Callaghan neighborhood graph . . . . . . . . . . . . . . . . . . . . . . . . . 29
xvii

xviii List of Figures
2.18 Representati v e cell graphs for small regions of H&E stained images (a) Simple
cell graph with
D = 200
for gastric tissue re gion (b) Probabilistic cell graph with
α = 2
for gastric tissue re gion (c) Hierarchical cell graph for breast tissue where
each v ertex represents a cell cluster with a corresponding simple cell graph b uilt
on the constituent cells (denoted by rectangle) . . . . . . . . . . . . . . . . . . 30
2.19 Attrib uted Relational Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.1 Schematic ov ervie w of the experimental pipeline . . . . . . . . . . . . . . . . 48
5.1 Examples of corresponding sections in (a) HER2 and (b) H&E stains. . . . . . 53
5.2
Screenshot of virtual microscopy program used by pathologists to create annota-
tions in HER2 WSI based on immunohistochemical response . . . . . . . . . . 54
5.3
Screenshot of V iSPEe program for semi-automatic registration in one HER2 and
H & E W S I p a i r .................................. 57
5.4
Mapping by linear triangular interpolation of (a) triangle
P 1 P 2 P 3
in the
x, y
plane to (b) triangle Q 1 Q 2 Q 3 in the u, v p l a n e . .................. 58
5.5
Example of HER2 and H&E WSI pair containing original and resulting patholo-
gists’ annotations after semi-automatic WSI re gistration and annotation transfor -
m a t i o n p r o c e d u r e ................................. 59
5.6
Screenshot of R OIManager program with cell nuclei annotations (yello w ‘+’
marks) made in an image tile at the highest resolution . . . . . . . . . . . . . . 61
5.7
Distrib ution of point annotations among the three types of re gions (number and
percentage of total annotations) . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.8
Defined cell nuclei classes (a) Epithelial cell (b) Leukoc yte (c) Fibroc yte (d)
Conglomerate (e) Fragment (f) Other cell (including blood cell in v essel) (g)
A r t e f a c t . ...................................... 62
5.9
Screenshot of the Object-Manager program in re vie w mode sho wing labeled
contours in an image tile at highest resolution . . . . . . . . . . . . . . . . . . 63
5.10
Distrib ution of cell nuclei annotations for se v en classes in each of the three
types of re gions and total annotations at (a) 30
×
objecti v e magnification (b) 40
×
o b j e c t i v e m a g n i fi c a t i o n . ............................. 63
5.11
Screenshots of (a) VM Slide Explorer program and an e xample WSI sho wing
rectangular re gions of interest in the three types of malignancy groups (HER2
positi v e: yello w , HER2 neg ati v e: blue and non-tumor: green), and necrotic
polygons marked inside them (red) at 0.65
×
objecti v e magnification (b) Object-
Manager plugin and an e xample of image tile from non-tumor region with square
annotations created at the smallest tile size, where necrotic tiles are mark ed with
blue squares and non-necrotic in green squares at 40 × objecti v e magnification. 65

List of Figures xix
5.12
(a) R OC characteristics for combined datasets using the described SVM-based
method sho wing comparati v e performance between dif ferent image tile sizes
(b) Distrib ution of labeled image tiles for necrotic and non-necrotic tissue in
three types of malignanc y regions and total image tiles in the final dataset for
S V M - b a s e d m e t h o d ................................ 67
5.13
Distrib ution of labeled image tiles for necrotic and non-necrotic tissue in HER2
ne gati v e tumor and non-tumor types of malignanc y regions and total image tiles
in the final dataset for deep learning methods . . . . . . . . . . . . . . . . . . 67
6.1 Schematic representation of the process of necrosis in a cell . . . . . . . . . . . 71
6.2
Image tile pair and te xture feature pictorial representation using (a) GLCM
statistics (b) Gabor filter kernels. . . . . . . . . . . . . . . . . . . . . . . . . . 73
6.3 P arameter selection using grid search for necrosis detection using SVM . . . . 75
6.4 Selection of discriminati ve thresholds for SVM classification . . . . . . . . . . 76
6.5 Proposed CNN architecture for necrosis detection . . . . . . . . . . . . . . . . 77
6.6
Examples of learning curv es of random training rounds for necrosis detection
using (a) AlexNet CNN frame work (b) proposed CNN architecture. . . . . . . 78
6.7
Cell nuclei se gmentation result example at 25
×
objecti v e magnification (a)
Original image (b) Processed image sho wing resulting cell nuclei se gments. . . 81
6.8
Example of cell nuclei se gmentation results of an image at dif ferent objecti v e
magnifications for visual inspection (a) 10
×
(b) 15
×
(c) 20
×
(d) 25
×
(e) 30
×
(f) 40 × . ...................................... 83
6.9 Cell nuclei segmentation performance at indi vidual magnifications . . . . . . . 83
6.10
Morphological feature definitions of a contour (a) Axes projections (b) Freeman
code. Adapted from [ Hufnagl 1984 ] . ....................... 86
6.11
P arameter selection using grid search for multi-class SVM classification of cell
n u c l e i ....................................... 90
6.12 P arameter selection for multi-class AdaBoost classification of cell nuclei . . . . 91
6.13 P arameter selection for multi-class random forest classification of cell nuclei . . 93
6.14 Flo wchart for multiresolution combination of cell nuclei se gmentation . . . . . 94
6.15 Cell nuclei se gmentation performance after multiresolution combination . . . . 95
6.16
Results for application of automatic necrosis detection on gastric cancer WSI
datasets in (a) HER2 positi v e tumor (b) HER2 ne gati v e tumor (c) Non-tumor . . 96
6.17
Example results of prototype application for automatic determination of tissue
composition in (a) HER2 positi v e tumor (b) HER2 neg ati v e tumor (c) Non-tumor .
97
7.1
Representati v e e xamples of image tiles from the same WSI of the defined
malignanc y le v els at the highest resolution for (a) HER2 positi ve tumor (b)
HER2 negati ve tumor (c) Non-tumor . . . . . . . . . . . . . . . . . . . . . . . 101

xx List of Figures
7.2
Distrib ution of labeled image tiles among the patients in HER2 positi v e tumor ,
HER2 ne gati v e tumor and non-tumor types of malignanc y re gions and total image
tiles for analysis of cancer re gions using traditional methods (a) at 1024
×
1024
pix el size (b) at 512 × 5 1 2 p i x e l s i z e . ....................... 103
7.3
Example of H&E WSI with (a) a fe w annotations of HER2 positi ve tumor mark ed
by e xpert pathologists in the corresponding HER2 WSI (b) a lo w magnified
(5
×
) re gion of agreement of most pathologists (c) e xample images after data
augmentation at highest magnification (40 × ) . .................. 104
7.4
Distrib ution of labeled image tiles among the patients in HER2 positi v e tumor ,
HER2 ne gati v e tumor and non-tumor types of malignanc y regions and total
image tiles for analysis of cancer re gions using deep learning methods . . . . . 104
7.5
Example of a HER2 neg ati v e tumor image tile, cell nuclei segments after se gmen-
tation procedure and the resulting V oronoi diagram and Delaunay triangulation
for high-lev el state-of-the-art feature extraction . . . . . . . . . . . . . . . . . 107
7.6 Schematic diagram of cell nuclei ARG . . . . . . . . . . . . . . . . . . . . . . 111
7.7 Uniform grid assumption for cell nuclei ARG with adapti v e r .......... 113
7.8
Example of an image tile from a lar ger R OI, its segmentation and representations
of eight cell nuclei ARG v ariants (the corresponding
ncARG
and
ncARG v +
ha v e same appearance b ut dif ference in the corresponding v ertex attrib utes) . . 118
7.9
T ypical example of malignanc y re gions o verlay with their
g AR G [ r F ]
,
r = 50
sho wing visible characteristics for (a) HER2 positi ve tumor (b) HER2 ne gati ve
t u m o r ( c ) N o n - t u m o r ............................... 119
7.10
Ale xNet CNN architecture implemented for cancer classification in gastric cancer
WSI datasets. Adapted from [ Krizhe vsk y 2012 ] ................. 126
7.11 Proposed CNN architecture for cancer classification in gastric cancer WSI . . . 128
7.12 Cascade operations in the k th con v olutional layer . . . . . . . . . . . . . . . . . 129
7.13
Classification strate gies for cancer classification (a) Single-stage classification
(b) Hierarchical classification. . . . . . . . . . . . . . . . . . . . . . . . . . . 135
7.14
Illustrati v e e xamples of classification result (a) Original H&E WSI with pathol-
ogists’ annotations for cancer classification based on IHC, and corresponding
probability maps using proposed CNN architecture for (a) HER2 positi v e tumor
(b) HER2 ne gati v e tumor (c) non-tumor at a lo w magnification (0.3 × ) . ..... 137
7.15 Schematic diagram of a CBIR system . . . . . . . . . . . . . . . . . . . . . . 138
7.16
Illustrati v e e xperimental results of prototype CBIR application as P-R curves
for cate gories (a) Non-tumor (b) T umors (c) HER2 positi ve tumor (d) HER2
ne gati v e tumor and (e) o verall result using the Manhattan distance metric. . . . 140
8.1 Schematic example of box and whisker plot . . . . . . . . . . . . . . . . . . . 149
8.2
Ov erall mean and standard error of classification accuracy for necrosis detection
e xperiments using (a) k-fold stratified shuf fled split cross v alidation (b) A verage
BCA for all methods ov er both cross validations. . . . . . . . . . . . . . . . . 149

List of Figures xxi
8.3
Ov erall mean and standard error of classification accuracy for cell nuclei clas-
sification e xperiments using (a) k-fold stratified shuf fled split and (b) lea ve-a-
sample-out cross v alidations (c) A verage BCA for all methods o v er both cross
v a l i d a t i o n s . .................................... 151
8.4
Ov erall mean and standard error of classification accuracy for cancer classifica-
tion e xperiments for GLCM statistics using (a) k-fold stratified shuf fled split and
(b) lea v e-a-patient-out cross v alidation (c) A v erage BCA for all methods ov er
b o t h c r o s s v a l i d a t i o n s . ............................... 157
8.5
Ov erall mean and standard error of classification accuracy for cancer classifi-
cation e xperiments for Gabor filter-bank responses using (a) k-fold stratified
shuf fled split and (b) lea v e-a-patient-out cross v alidation (c) A verage BCA for
all methods ov er both cross v alidations. . . . . . . . . . . . . . . . . . . . . . 158
8.6
Ov erall mean and standard error of classification accuracy for cancer classifica-
tion e xperiments for LBP histograms using (a) k-fold stratified shuf fled split and
(b) lea v e-a-patient-out cross v alidation (c) A v erage BCA for all methods ov er
b o t h c r o s s v a l i d a t i o n s . ............................... 159
8.7
Ov erall mean and standard error of classification accuracy for cancer classifica-
tion e xperiments for V arma-Zisserman te xtons using (a) k-fold stratified shuf fled
split and (b) lea v e-a-patient-out cross v alidation (c) A verage BCA for all methods
o v e r b o t h c r o s s v a l i d a t i o n s ............................. 160
8.8
Ov erall mean and standard error of classification accuracy for cancer classifica-
tion e xperiments for gray histograms using (a) k-fold stratified shuf fled split and
(b) lea v e-a-patient-out cross v alidation (c) A v erage BCA for all methods ov er
b o t h c r o s s v a l i d a t i o n s . ............................... 160
8.9
Ov erall mean and standard error of classification accuracy for cancer classifica-
tion e xperiments for HSV histograms using (a) k-fold stratified shuf fled split and
(b) lea v e-a-patient-out cross v alidation (c) A v erage BCA for all methods ov er
b o t h c r o s s v a l i d a t i o n s . ............................... 161
8.10
Ov erall mean and standard error of classification accuracy of cancer classification
e xperiments for RGB histograms using (a) k-fold stratified shuf fled split and (b)
lea v e-a-patient-out cross v alidation (c) A verage BCA for all methods o v er both
c r o s s v a l i d a t i o n s . ................................. 162
8.11
Ov erall mean and standard error of classification accuracy for cancer classifica-
tion e xperiments for other color-based measurements using (a) k-fold stratified
shuf fled split and (b) lea v e-a-patient-out cross v alidation (c) A verage BCA for
all methods ov er both cross v alidations. . . . . . . . . . . . . . . . . . . . . . 163
8.12
Ov erall mean and standard error of classification accuracy for malignanc y re-
gion classification e xperiments for V oronoi-Delaunay method using (a) k-fold
stratified shuf fled split and (b) lea v e-a-patient-out cross v alidation (c) A verage
BCA for all methods ov er both cross validations. . . . . . . . . . . . . . . . . 164

xxii List of Figures
8.13
Ov erall mean and standard error of classification accuracy for re gion classifica-
tion e xperiments for only verte x attrib utes using (a) k-fold stratified shuf fled split
and (b) lea v e-a-patient-out cross v alidation (c) A verage BCA for all methods
o v e r b o t h c r o s s v a l i d a t i o n s ............................. 165
8.14
Ov erall mean and standard error of classification accuracy for re gion classifica-
tion e xperiments for only verte x identities using (a) k-fold stratified shuf fled split
and (b) lea v e-a-patient-out cross v alidation (c) A verage BCA for all methods
o v e r b o t h c r o s s v a l i d a t i o n s ............................. 166
8.15
Ov erall mean and standard error of classification accuracy for malignanc y re gion
classification e xperiments for
g AR G [ r F ]
using (a) k-fold stratified shuf fled split
and (b) lea v e-a-patient-out cross v alidation (c) A verage BCA for all methods
o v e r b o t h c r o s s v a l i d a t i o n s ............................. 167
8.16
Ov erall mean and standard error of classification accuracy for malignanc y re gion
classification e xperiments for
g AR G [ r A ]
using (a) k-fold stratified shuf fled split
and (b) lea v e-a-patient-out cross v alidation (c) A verage BCA for all methods
o v e r b o t h c r o s s v a l i d a t i o n s ............................. 168
8.17
Ov erall mean and standard error of classification accuracy for malignanc y re gion
classification e xperiments for
nsARG [ r F ]
using (a) k-fold stratified shuf fled
split and (b) lea v e-a-patient-out cross v alidation (c) A verage BCA for all methods
o v e r b o t h c r o s s v a l i d a t i o n s ............................. 168
8.18
Ov erall mean and standard error of classification accuracy for malignanc y re gion
classification e xperiments for
nsARG [ r A ]
using (a) k-fold stratified shuf fled
split and (b) lea v e-a-patient-out cross v alidation (c) A verage BCA for all methods
o v e r b o t h c r o s s v a l i d a t i o n s ............................. 169
8.19
Ov erall mean and standard error of classification accuracy for re gion classifica-
tion e xperiments for
ncARG [ r F ]
using (a) k-fold stratified shuf fled split and (b)
lea v e-a-patient-out cross v alidation (c) A verage BCA for all methods o v er both
c r o s s v a l i d a t i o n s . ................................. 170
8.20
Ov erall mean and standard error of classification accuracy for re gion classifica-
tion e xperiments for
ncARG [ r A ]
using (a) k-fold stratified shuf fled split and (b)
lea v e-a-patient-out cross v alidation (c) A verage BCA for all methods o v er both
c r o s s v a l i d a t i o n s . ................................. 171
8.21
Ov erall mean and standard error of classification accuracy for re gion classifica-
tion e xperiments for
ncARG v + [ r F ]
using (a) k-fold stratified shuf fled split and
(b) lea v e-a-patient-out cross v alidation (c) A v erage BCA for all methods ov er
b o t h c r o s s v a l i d a t i o n s . ............................... 172
8.22
Ov erall mean and standard error of classification accuracy for re gion classifica-
tion e xperiments for
ncARG v + [ r A ]
using (a) k-fold stratified shuf fled split and
(b) lea v e-a-patient-out cross v alidation c) A v erage BCA for all methods ov er
b o t h c r o s s v a l i d a t i o n s . ............................... 173

List of Figures xxiii
8.23
Ov erall mean and standard error of classification accuracy for cancer classifica-
tion e xperiments for selected subset of hybrid lo w-le vel features using (a) k-fold
stratified shuf fled split and (b) lea v e-a-patient-out cross v alidation (c) A verage
BCA for all methods ov er both cross validations. . . . . . . . . . . . . . . . . 174
8.24
Ov erall mean and standard error of classification accuracy for cancer classifica-
tion e xperiments for selected subset of hybrid lo w-le vel and high-le vel features
using (a) k-fold stratified shuf fled split and (b) lea v e-a-patient-out cross v alida-
tion (c) A verage BCA for all methods o v er both cross v alidations. . . . . . . . 175
8.25
Ov erall mean and standard error of classification accuracy for re gion classifica-
tion e xperiments for deep learning methods using (a) k-fold stratified shuf fled
split and (b) lea v e-a-patient-out cross v alidation (c) A verage BCA for all methods
o v e r b o t h c r o s s v a l i d a t i o n s ............................. 176
8.26
Ov erall mean and standard error of per-class classification accurac y for all the
methods ov er both cross v alidations . . . . . . . . . . . . . . . . . . . . . . . 178
8.27
Magnified line diagram sho wing a v erage BCA for the dif ferent feature groups
and corresponding traditional machine learning strate gies . . . . . . . . . . . . 179
8.28
Ov erall mean and standard error of balanced classification accuracy for tradi-
tional machine learning (using hybrid features) and deep learning methods . . . 181
9.1
Representati v e e xamples of images in three malignancy le vels using HER2 stain
(a) HER2 positi ve tumor (b) HER2 negati ve tumor (c) Non-tumor . . . . . . . . 185
9.2
Heatmaps of the e xtracted feature vectors sho wing image statistics in the HER2
stain (a) GLCM statistics (b) LBP histograms (c) HSV histograms (d) RGB
h i s t o g r a m s . .................................... 185
9.3
Representati v e e xamples of images in three malignancy le vels using H&E stain
(a) HER2 positi ve tumor (b) HER2 negati ve tumor (c) Non-tumor . . . . . . . . 187
9.4
Heatmaps of the e xtracted feature vectors sho wing image statistics in the HER2
stain (a) GLCM statistics (b) LBP histograms (c) HSV histograms (d) RGB
histograms (e) V arma-Zisserman textons (f) V oronoi diagram and Delaunay
triangulations (g) nsARG [ r F ] features (h) selected subset of hybrid features. . 188
A.1
A verage e x ecution time requirements of graph-based constructions for V oronoi-
Delaunay and g AR G [ r F ] graphs and v ariations with the number of graph vertices 211
A.2
A verage e xecution time requirements (log-linear plot) of traditional machine
learning methods and v ariations with number of features during training . . . . 211
A.3
Representati v e examples of metadata screenshots generated during (a) semi-
automatic WSI re gistration (XML file) (b) annotations for cell nuclei segmenta-
tion e v aluation (R OI file) (c) annotations and features for cell nuclei classification
( X M L l e a r n i n g s a m p l e fi l e ) ............................ 212
B.1 Color scale for values in confusion matrices . . . . . . . . . . . . . . . . . . . 215

xxiv List of Figures
B.2
Box and whisker diagrams of a verage per -class classification accurac y for necro-
sis detection e xperiments using k-fold stratified shuf fled split cross v alidation
for (a) necrosis (b) non-necrosis. . . . . . . . . . . . . . . . . . . . . . . . . . 225
B.3
Box and whisker diagrams of a verage per -class classification accurac y for cell
nuclei classification e xperiments using k-fold stratified shuf fled split cross v ali-
dation for a) epithelial cell (b) leukoc yte (c) fibroc yte (or border cell) (d) con-
glomerate (e) fragment (f) other cell (including blood cell in v essel) (g) artefact. 225
B.4
Box and whisker diagrams of a verage per -class classification accurac y of GLCM
statistics for (a) HER2 positi v e tumor (b) HER2 neg ati v e tumor (c) Non-tumor
using k-fold stratified shuf fled split and (d),(e),(f) using lea v e-a-patient-out cross
v a l i d a t i o n s . .................................... 226
B.5
Box and whisker diagrams of a v erage per -class classification accurac y of Gabor
filter -bank responses for (a) HER2 positi v e tumor (b) HER2 neg ati v e tumor
(c) Non-tumor using k-fold stratified shuf fled split and (d),(e),(f) using lea v e-a-
patient-out cross v alidations. . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
B.6
Box and whisker diagrams of a verage per -class classification accurac y of local
binary patterns for (a) HER2 positi v e tumor (b) HER2 ne gati v e tumor (c) Non-
tumor using k-fold stratified shuf fled split and (d),(e),(f) using lea v e-a-patient-out
c r o s s v a l i d a t i o n s . ................................. 227
B.7
Box and whisker diagrams of a v erage per -class classification accurac y of V arma-
Zissserman te xtons for (a) HER2 positi v e tumor (b) HER2 negati ve tumor (c)
Non-tumor using k-fold stratified shuf fled split and (d),(e),(f) using lea v e-a-
patient-out cross v alidations. . . . . . . . . . . . . . . . . . . . . . . . . . . . 228
B.8
Box and whisker diagrams of a v erage per -class classification accurac y of gray
histograms for (a) HER2 positi v e tumor (b) HER2 neg ati v e tumor (c) Non-tumor
using k-fold stratified shuf fled split and (d),(e),(f) using lea v e-a-patient-out cross
v a l i d a t i o n s . .................................... 228
B.9
Box and whisker diagrams of a verage per -class classification accurac y of HSV
histograms for (a) HER2 positi v e tumor (b) HER2 neg ati v e tumor (c) Non-tumor
using k-fold stratified shuf fled split and (d),(e),(f) using lea v e-a-patient-out cross
v a l i d a t i o n s . .................................... 229
B.10
Box and whisker diagrams of a verage per -class classification accurac y of RGB
histograms for (a) HER2 positi v e tumor (b) HER2 neg ati v e tumor (c) Non-tumor
using k-fold stratified shuf fled split and (d),(e),(f) using lea v e-a-patient-out cross
v a l i d a t i o n s . .................................... 229
B.11
Box and whisker diagrams of a verage per -class classification accurac y of other
color -based measurements for (a) HER2 positi v e tumor (b) HER2 neg ati v e
tumor (c) Non-tumor using k-fold stratified shuf fled split and (d),(e),(f) using
lea v e-a-patient-out cross validations. . . . . . . . . . . . . . . . . . . . . . . 230

List of Figures xxv
B.12
Box and whisker diagrams of a v erage per -class classification accurac y for the
V oronoi-Delaunay method for (a) HER2 positi v e tumor (b) HER2 ne gati v e
tumor (c) Non-tumor using k-fold stratified shuf fled split and (d),(e),(f) using
lea v e-a-patient-out cross validations. . . . . . . . . . . . . . . . . . . . . . . 230
B.13
Box and whisker diagrams of a verage per -class classification accurac y for only
v ertex attrib utes of cell nuclei ARG for (a) HER2 positi ve tumor (b) HER2
ne gati v e tumor (c) Non-tumor using k-fold stratified shuf fled split and (d),(e),(f)
using leav e-a-patient-out cross v alidations. . . . . . . . . . . . . . . . . . . . 231
B.14
Box and whisker diagrams of a verage per -class classification accurac y for only
v ertex identities (or tissue composition) of cell nuclei ARG for (a) HER2 positi ve
tumor (b) HER2 neg ati v e tumor (c) Non-tumor using k-fold stratified shuf fled
split and (d),(e),(f) using lea ve-a-patient-out cross validations. . . . . . . . . . 231
B.15
Box and whisker diagrams of a verage per -class classification accurac y for hand-
crafted
g AR G [ r F ]
method for (a) HER2 positi v e tumor (b) HER2 neg ati v e
tumor (c) Non-tumor using k-fold stratified shuf fled split and (d),(e),(f) using
lea v e-a-patient-out cross validations. . . . . . . . . . . . . . . . . . . . . . . 232
B.16
Box and whisker diagrams of a verage per -class classification accurac y for hand-
crafted
g AR G [ r A ]
method for (a) HER2 positi v e tumor (b) HER2 neg ati v e
tumor (c) Non-tumor using k-fold stratified shuf fled split and (d),(e),(f) using
lea v e-a-patient-out cross validations. . . . . . . . . . . . . . . . . . . . . . . 232
B.17
Box and whisker diagrams of a verage per -class classification accurac y for hand-
crafted
nsARG [ r F ]
method for (a) HER2 positi v e tumor (b) HER2 neg ati v e
tumor (c) Non-tumor using k-fold stratified shuf fled split and (d),(e),(f) using
lea v e-a-patient-out cross validations. . . . . . . . . . . . . . . . . . . . . . . 233
B.18
Box and whisker diagrams of a verage per -class classification accurac y for hand-
crafted
nsARG [ r A ]
method for (a) HER2 positi v e tumor (b) HER2 ne gati v e
tumor (c) Non-tumor using k-fold stratified shuf fled split and (d),(e),(f) using
lea v e-a-patient-out cross validations. . . . . . . . . . . . . . . . . . . . . . . 233
B.19
Box and whisker diagrams of a verage per -class classification accurac y for hand-
crafted
ncARG [ r F ]
method for (a) HER2 positi v e tumor (b) HER2 neg ati v e
tumor (c) Non-tumor using k-fold stratified shuf fled split and (d),(e),(f) using
lea v e-a-patient-out cross validations. . . . . . . . . . . . . . . . . . . . . . . 234
B.20
Box and whisker diagrams of a verage per -class classification accurac y for hand-
crafted
ncARG [ r A ]
method for (a) HER2 positi v e tumor (b) HER2 ne gati v e
tumor (c) Non-tumor using k-fold stratified shuf fled split and (d),(e),(f) using
lea v e-a-patient-out cross validations. . . . . . . . . . . . . . . . . . . . . . . 234
B.21
Box and whisker diagrams of a verage per -class classification accurac y for hand-
crafted
ncARG v + [ r F ]
method for (a) HER2 positi v e tumor (b) HER2 ne gati v e
tumor (c) Non-tumor using k-fold stratified shuf fled split and (d),(e),(f) using
lea v e-a-patient-out cross validations. . . . . . . . . . . . . . . . . . . . . . . 235

xxvi List of Figures
B.22
Box and whisker diagrams of a verage per -class classification accurac y for hand-
crafted
ncARG v + [ r A ]
method for (a) HER2 positi v e tumor (b) HER2 ne gati v e
tumor (c) Non-tumor using k-fold stratified shuf fled split and (d),(e),(f) using
lea v e-a-patient-out cross validations. . . . . . . . . . . . . . . . . . . . . . . 235
B.23
Box and whisker diagrams of a v erage per -class classification accurac y of selected
subset of hybrid lo w-le vel features for (a) HER2 positi ve tumor (b) HER2
ne gati v e tumor (c) Non-tumor using k-fold stratified shuf fled split and (d),(e),(f)
using leav e-a-patient-out cross v alidations. . . . . . . . . . . . . . . . . . . . 236
B.24
Box and whisker diagrams of a v erage per -class classification accurac y of selected
subset of hybrid lo w-lev el and high-le vel features for (a) HER2 positi ve tumor
(b) HER2 ne gati v e tumor (c) Non-tumor using k-fold stratified shuf fled split and
(d),(e),(f) using leav e-a-patient-out cross v alidations. . . . . . . . . . . . . . . 236
B.25
Box and whisker diagrams of a v erage per -class classification accurac y for deep
learning methods for (a) HER2 positi v e tumor (b) HER2 neg ati v e tumor (c) Non-
tumor using k-fold stratified shuf fled split and (d),(e),(f) using lea v e-a-patient-out
c r o s s v a l i d a t i o n s . ................................. 237

List of T ables
2.1 GLCM statistical descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2
Summary of popular algorithms for constructing V oronoi diagram, Delaunay
triangulation and its subgraphs . . . . . . . . . . . . . . . . . . . . . . . . . . 26
5.1
Distrib ution of number of pathologists’ annotations marked in HER2 WSI on the
basis of immunohistochemical response (P: pathologist, S: slide, H+: number of
HER2+ tumor annotations, H-: number of HER2- tumor annotations) . . . . . 56
5.2
Number of image tiles of dif ferent sizes in the three datasets for necrosis detection
u s i n g S V M - b a s e d m e t h o d ............................. 65
6.1 P arameter selection in cell nuclei segmentation algorithm . . . . . . . . . . . . 81
6.2 List of object-le vel features computed for cell nuclei classification . . . . . . . 88
6.3 Rele v ance scores assigned to classified cell nuclei objects . . . . . . . . . . . . 94
7.1
Cancer interpretation based on graphs: summary of visible characteristics and
corresponding cell nuclei ARG properties . . . . . . . . . . . . . . . . . . . . 117
7.2
Results for feature selection based on correlation analysis after combination of
f e a t u r e s ...................................... 122
7.3
Details of most successful empirically e v aluated CNN architectures for cancer
classification on gastric cancer representativ e datasets . . . . . . . . . . . . . . 127
7.4
Examples of two-dimensional outputs in the three con v olutional layers of the
p r o p o s e d C N N a r c h i t e c t u r e ............................ 131
7.5 Summary of parameter selection for the proposed CNN architecture . . . . . . 132
7.6
Per -class e xperimental results sho wing performance measures for the prototype
C B I R a p p l i c a t i o n ................................. 140
8.1 Elements in the confusion matrix M ....................... 147
8.2
Scheme of comparati v e e v aluation of studied methods for analysis of cancer
r e g i o n s ....................................... 154
A.1
Important program modules, supporting software and computational require-
ments of the proposed frame work . . . . . . . . . . . . . . . . . . . . . . . . 205
xxvii

xxviii List of T ables
B.1
A verage accurac y confusion matrix using k-fold stratified shuf fled split cross val-
idation for (a) SVM with discriminati v e thresholds (b) Ale xNet CNN frame w ork
(c) Proposed CNN architecture (d) Ensemble of CNNs . . . . . . . . . . . . . 216
B.2
A verage accurac y confusion matrix for SVM classification method using k-fold
stratified shuffled split cross v alidation . . . . . . . . . . . . . . . . . . . . . . 216
B.3
A verage accurac y confusion matrix for SVM classification method using lea v e-
a-sample-out cross v alidation . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
B.4
A verage accurac y confusion matrix for AdaBoost ensemble classification using
k-fold stratified shuf fled split cross v alidation . . . . . . . . . . . . . . . . . . 217
B.5
A verage accurac y confusion matrix for AdaBoost ensemble classification using
lea v e-a-sample-out cross validation . . . . . . . . . . . . . . . . . . . . . . . . 217
B.6
A verage accurac y confusion matrix for random forest classification using k-fold
stratified shuffled split cross v alidation . . . . . . . . . . . . . . . . . . . . . . 217
B.7
A verage accurac y confusion matrix for random forest classification using lea v e-
a-sample-out cross v alidation . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
B.8
A verage accurac y confusion matrix for GLCM statistics with SVM hierarchical
classification (each stage) using (a) k-fold stratified shuf fled split (b) lea v e-a-
patient-out cross v alidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
B.9
A verage accurac y confusion matrix for Gabor filter -bank responses with SVM
hierarchical classification (each stage) using (a) k-fold stratified shuf fled split (b)
lea v e-a-patient-out cross validation . . . . . . . . . . . . . . . . . . . . . . . . 219
B.10
A verage accurac y confusion matrix for local binary patterns with SVM single-
stage classification using (a) k-fold stratified shuf fled split (b) lea v e-a-patient-out
c r o s s v a l i d a t i o n .................................. 219
B.11
A verage accurac y confusion matrix for V arma-Zisserman textons with SVM
single-stage classification (a) k-fold stratified shuf fled split (b) lea v e-a-patient-
o u t c r o s s v a l i d a t i o n ................................ 219
B.12
A verage accurac y confusion matrix for gray histograms with random forests
single-stage classification (a) k-fold stratified shuf fled split (b) lea v e-a-patient-
o u t c r o s s v a l i d a t i o n ................................ 219
B.13
A verage accurac y confusion matrix for HSV histograms with AdaBoost single-
stage classification using (a) k-fold stratified shuf fled split (b) lea v e-a-patient-out
c r o s s v a l i d a t i o n .................................. 220
B.14
A verage accurac y confusion matrix for RGB histograms with SVM hierarchical
classification using (a) k-fold stratified shuf fled split (b) lea v e-a-patient-out cross
v a l i d a t i o n ..................................... 220
B.15
A verage accurac y confusion matrix for other color -based measurements with
random forests single-stage classification using (a) k-fold stratified shuf fled split
(b) leav e-a-patient-out cross v alidation . . . . . . . . . . . . . . . . . . . . . . 220

List of T ables xxix
B.16
A verage accurac y confusion matrix for V oronoi-Delaunay method with random
forest single-stage classification using (a) k-fold stratified shuf fled split (b) lea v e-
a-patient-out cross v alidation . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
B.17
A verage accurac y confusion matrix for only verte x attrib utes (object-le vel fea-
tures) of cell nuclei ARG with random forest hierarchical classification (each
stage) using (a) k-fold stratified shuf fled split (b) lea v e-a-patient-out cross v alidation
221
B.18
A verage accurac y confusion matrix for only v erte x identities (tissue composition)
of the cell nuclei ARG with SVM single-stage classification using (a) k-fold
stratified shuffled split (b) leav e-a-patient-out cross v alidation . . . . . . . . . 221
B.19
A verage accurac y confusion matrix for handcrafted
g AR G [ r F ]
method with
SVM single-stage classification using (a) k-fold stratified shuf fled split (b) lea v e-
a-patient-out cross v alidation . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
B.20
A verage accurac y confusion matrix for handcrafted
g AR G [ r A ]
method with
SVM single-stage classification using (a) k-fold stratified shuf fled split (b) lea v e-
a-patient-out cross v alidation . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
B.21
A verage accurac y confusion matrix for handcrafted
nsARG [ r F ]
method with
SVM single-stage classification using (a) k-fold stratified shuf fled split (b) lea v e-
a-patient-out cross v alidation . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
B.22
A verage accurac y confusion matrix for handcrafted
nsARG [ r A ]
method with
SVM hierarchical classification (each stage) using (a) k-fold stratified shuf fled
split (b) leav e-a-patient-out cross v alidation . . . . . . . . . . . . . . . . . . . 222
B.23
A verage accurac y confusion matrix for handcrafted
ncARG [ r F ]
method with
SVM single-stage classification using (a) k-fold stratified shuf fled split (b) lea v e-
a-patient-out cross v alidation . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
B.24
A verage accurac y confusion matrix for handcrafted
ncARG [ r A ]
method with
random forest hierarchical classification using (a) k-fold stratified shuf fled split
(b) leav e-a-patient-out cross v alidation . . . . . . . . . . . . . . . . . . . . . . 222
B.25
A verage accurac y confusion matrix for handcrafted
ncARG v + [ r F ]
method with
SVM single-stage classification using (a) k-fold stratified shuf fled split (b) lea v e-
a-patient-out cross v alidation . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
B.26
A verage accurac y confusion matrix for handcrafted
ncARG v + [ r A ]
method with
SVM single-stage classification using (a) k-fold stratified shuf fled split (b) lea v e-
a-patient-out cross v alidation . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
B.27
A verage accurac y confusion matrix for selected subset of hybrid lo w-le vel fea-
tures with SVM hierarchical classification using (a) k-fold stratified shuf fled
split (b) leav e-a-patient-out cross v alidation . . . . . . . . . . . . . . . . . . . 223
B.28
A verage accurac y confusion matrix for selected subset of hybrid lo w-le vel and
high-le v el features with SVM single-stage classification using (a) k-fold stratified
shuf fled split (b) lea v e-a-patient-out cross v alidation . . . . . . . . . . . . . . . 223

xxx List of T ables
B.29
A verage accurac y confusion matrix for Ale xNet CNN frame work in single-stage
classification using (a) k-fold stratified shuf fled split (b) lea v e-a-patient-out cross
v a l i d a t i o n ..................................... 224
B.30
A verage accurac y confusion matrix for proposed CNN architecture in single-
stage classification using (a) k-fold stratified shuf fled split (b) lea v e-a-patient-out
c r o s s v a l i d a t i o n .................................. 224
B.31
A verage accurac y confusion matrix for ensemble of CNNs in single-stage clas-
sification using (a) k-fold stratified shuf fled split (b) lea v e-a-patient-out cross
v a l i d a t i o n ..................................... 224

List of Ab br e viations
Acr onyms / Abbr e viations
AB-HC AdaBoost Hierarchical Classification
AB-SC AdaBoost Single-stage Classification
AdaBoost Adapti ve Boosting
ARG Attrib uted Relational Graph
BCA Balanced Classification Accurac y
CAD Computer -aided Diagnosis
CBIR Content-based Image Retrie v al
CNN Con v olutional Neural Network
CPU Central Processing Unit
ECM Extra-cellular Matrix
gARG Generic Attrib uted Relational Graph
gARG[r A ] Generic Attributed Relational Graph and Adapti ve r
gARG[r F ] Generic Attributed Relational Graph and Fix ed r
GLCM Gray Le v el Co-occurrence Matrix
GPU Graphics Processing Unit
H&E Haematoxylin and Eosin
HER2 Human epidermal gro wth factor receptor 2
HSV Hue Saturation V alue
IDE Inte grated De v elopment En vironment
xxxi

xxxii List of Abbre viations
IHC Immunohistochemistry
LBP Local Binary P atterns
LMDB Lightening Memory-mapped Database
LOO Lea v e-one-out
MR8 Maximum Response 8
MST Minimum Spanning T ree
ncARG Nuclei-composite Attributed Relational Graph
ncARG[r A ] Nuclei-composite Attributed Relational Graph and Adapti ve r
ncARG[r F ] Nuclei-composite Attributed Relational Graph and Fix ed r
ncARG v+ Nuclei-composite Attrib uted Relational Graph with additional v ertex attrib utes
ncARG v+ [r A ]
Nuclei-composite Attrib uted Relational Graph with additional v ertex attrib utes
and Adapti v e r
ncARG v+ [r F ]
Nuclei-composite Attrib uted Relational Graph with additional verte x attrib utes
and Fix ed r
NCCD Nomenclature Committee on Cell Death
NNG Nearest Neighbor Graph
nsARG Nuclei-specific Attributed Relational Graph
nsARG[r A ] Nuclei-specific Attributed Relational Graph and Adapti ve r
nsARG[r F ] Nuclei-specific Attributed Relational Graph and Fix ed r
OCA Ov erall Classification Accuracy
OOB Out-of-bag
PDF Probability Density Function
P-R Precision-Recall
r A Adapti v e r
RBF Radial Basis Function
ReLU Rectified Linear Unit
RF-HC Random Forest Hierarchical Classification

List of Abbre viations xxxiii
r F Fix ed r
RF-SC Random Forest Single-stage Classification
RFS Root Filter Set
RGB Red Blue Green
RNG Relati v e Neighborhood Graph
R OC Recei ver Operating Characteristics
R OI Re gion of Interest
SDK Software De velopment Kit
SVD Singular V alue Decomposition
SVM-HC Support V ector Machine Hierarchical Classification
SVM-SC Support V ector Machine Single-stage Classification
SVM Support V ector Machine
TIFF T agged Image File Format
V iSPEe V irtual Slide Processing En vironment
VSF V irtual Slide Format
VZ V arma Zisserman
WSI Whole Slide Image
XML Extensible Markup Language

C H A P T E R 1
Intr oduction
Contents
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Study Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Motiv ations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Contrib utions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4.1 Scientific Contributions . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4.2 Application Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.5 Challenges in Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.6 Organization of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.1 Backgr ound
Histological images are the magnified images of tissues of an or ganism’ s body . These are a
special kind of medical images, which contain v arious comple x structures and the underlying
semantic kno wledge pertaining to dif ferent biological conditions of the org anism. Histopathology
is the field of e xamining tissue specimen by medical specialists (pathologists) to determine
the presence and e xtent of abnormal conditions, especially tumors and cancers. Generally , a
pathological test is recommended after observing physiological symptoms of a subject, and
performed by acquiring histopathological specimens from the human body through biopsy or
sur gical resection [
W eidner 2009
]. The tissue specimens are mounted on glass slides and treated
with detailed preparation procedure before vie wing [
Rolls 2011
]. This includes a staining process
with special chemicals in order to impro ve contrast and visibility of particular histological and
cellular structures as required for observ ation. Con v entional vie wing and e xamination of glass
slides is performed using di v erse microscopy techniques [ Rocho w 1994 ].
Digital pathology as a sub-branch of pathology in v olves the use of digitized histological
images for observ ation and analysis. Glass slides are first scanned using sophisticated equipment
1

2 CHAPTER 1. Introduction
such as adv anced microscope cameras or whole slide scanners, as a result of which high resolution
whole slide images (WSI) are obtained. These images are stored on local computers or remote
serv ers, observed on screens using vie wing programs, and archiv ed into lar ge databases for
future references. They pro vide a comprehensi v e vie w of the tissue architecture, allo wing the
pathologists to form opinions about the underlying disease conditions. Se v eral benefits of digital
pathology ha v e been identified, the most apparent being con venient slide na vigation, ef ficient
data handling, parallel vie wing at distinct locations (telepathology), parallel vie wing of dif ferent
stains and positions, reduction in glass archi v es and glass transportation, and simplicity to handle
pathologists’ annotations [ Sucaet 2014 ].
Medical image analysis techniques are being de v eloped to automate the process of e xtracting
meaningful information from whole slide images for clinical routine practice and research in
digital pathology . Majority of the research in this direction has been aimed to w ards de v eloping
computer -aided diagnostic methods in order to automatically predict tissue re gions of interest
(R OI) as healthy or cancerous, benign or malignant, or one of the tumor grades or types. Less
critical applications are also being e xplored that can potentially impro ve the current state of
technology in this field, for instance, content-based image retrie v al of interest re gions from
whole slide image data, archi ving and bio-banking of large databases, marker quantification
and detecting malignant changes o ver time, thereby , contributing directly or indirectly to wards
diagnosis, prognosis, education and research in biology and medicine. The de veloped computer -
based tools and techniques provide assistance to pathologists by reducing their time inputs,
manual ef forts and subjecti ve v ariations. Ne vertheless, pathologists’ e xperience and e xpertise are
most v aluable and essential for the de velopment of these image processing and analysis software,
as well as for the v alidation of obtained results.
1.2 Study Objectiv es
In this study , the prime objecti ve is recognized as the de velopment of a fully-automated systematic
frame w ork to perform medical image analysis of tissue regions in whole slide images of gastric
cancer . The sub-objectiv es of the research are described as the follo wing.
1.
The comprehensi v e research goal is to explore suitable medical image analysis algorithms,
leading to ef ficient kno wledge description and representation of meaningful visual information
for automatic cate gorization and retrie v al in histopathological whole slide images of gastric
cancer . This demands the study and modifications of e xisting methods, and the proposal
of no vel approaches with theoretical descriptions and e xperimental e vidence for further
enhancement in current state of technology .
2.
In this work, a prime objecti ve is the analysis of cancer -affected tissue re gions using the H&E
stain based on HER2 immunohostochemical response in gastric cancer whole slide images.
H&E stained images need to be thoroughly analyzed in order to determine whether these are
suf ficient for cancer characterization on the basis of HER2 immunohistochemistry , and to
establish if an y visible correlations exist between the tw o stains. Thus, computer -based image

1.3 Moti v ations 3
analysis of gastric cancer in H&E stain requires to be pursued in depth through traditional
methods as well as deep learning methods in digital histopathology .
3.
During the research tasks, related goals hav e been disco v ered, namely , computer -based
e xclusion of necrotic tissue regions and ef fectiv e cell nuclei se gmentation. F or necrosis
detection, an adequate and timely solution needs to be de vised. Also, the H&E stain has
the property to distinctly stain the cell nuclei in the tissue specimen, so, for the automatic
isolation of cell nuclei, a se gmentation algorithm is required to be extensi vely e valuated. This
in v olves determining the w orking magnification and image resolution suitable for optimum
image analysis. Further , a multiresolution approach needs to be explored for enhancement of
se gmentation results compared to indi vidual magnifications.
4.
Computerized applications corresponding to the abo ve defined problems can greatly assist pro-
fessionals in routine and research, thus, a part of this study is dedicated to wards contrib ution
of application-specific software tools. This includes demonstrating example applications such
as computer -aided diagnosis, automatic necrosis detection, tissue composition determination
and content-based image retrie v al in H&E stained gastric cancer whole slide images.
1.3 Moti vations
In the current scenario, pathologists mostly examine collections of lar ge-sized whole slide images
(or glass slides) using visual inspection, which is a tedious and time-consuming process that
requires to be automated. Therefore, the significance of histological image analysis has been
recognized by the scientific community to a great e xtent worldwide. Ho we v er , compared to other
types of medical images, research on histological images is some what limited. This is because
histological images ha v e typical characteristics which set them apart from other types of images.
These include di v erse magnifications, complex appearances, large image resolutions, highly
specialized acquisition process, specific staining methods and the corresponding dif ferences in
semantic interpretations. Furthermore, there is alw ays a possibility of human errors in analyzing
histological images due to high subjecti vity and observ er v ariations during interpretation o wing
to a high visual similarity of such images, which may subsequently af fect the selection of an
appropriate treatment method. Most visual observ ations vary from person to person and time
to time, leading to inter - and intra-observ er v ariability and ambiguities in the decision-making
process. Hence, there is clearly a need for de v eloping computer -aided methods to introduce
objecti vity , which is a strong moti v ation behind this work.
Gastric cancer is the fourth most common cancer and the third most common cause of
cancer -related deaths in the world. According to the WHO, gastric cancer accounts for around
800,000 deaths worldwide annually [
Rugge 2015
]. Computer-aided analysis of gastric cancer
histological images is still in its early stages of de v elopment and an acti vely emer ging topic of
research. In literature, a fe w studies ha v e been performed in the direction of gastric tissue image

4 CHAPTER 1. Introduction
analysis in digital histopathology
1
. Ho we v er , to the best of the author’ s knowledge, no pre vious
study focuses on the specified research objecti v es as described in this w ork. This moti v ated the
author to proceed in the direction of medical image analysis in gastric cancer histopathology .
Haematoxylin and eosin (H&E) stain is routinely used in histopathological e xaminations as it
pro vides a detailed vie w of tissue components, is easy to apply and less costly . In contrast, HER2
immunohistochemical staining is not commonly applied in laboratory practice and in v olves
higher costs. For this study , both HER2 and H&E stained sections were av ailable but H&E
stain has been analyzed because of its wider usability and lo wer preparation costs. When HER2
sections are observ ed by pathologists using optical microscopy techniques, cancer cate gories
are mostly visually distinguishable. Howe ver , while observing H&E stained tissue sections,
pathologists require a greater time and ef fort to identify the corresponding malignanc y le v els due
to v ery subtle dif ferences, hence, immunostaining is often suggested. One of the main moti v ations
of this study is to discriminate between cancer types using computer -based image analysis in the
H&E stained tissue re gions based on HER2 immunohistochemistry , with the assumption that
minor de viations in the te xture, color , morphological, neighborhood and architectural properties
of tissues that may be dif ficult and laborious to recognize barely with the human e ye, can allo w
sophisticated computerized methods to make desired decisions.
One of the most promising approaches for histological image analysis is the use of graph-
based techniques [
Sharma 2015a
]. Graphs are ef fecti ve and fle xible representation structures and
ha v e lately been of major interest to the computer vision and image analysis fields due to their
e xpressi v e ability to model topological and relational information between image components.
Moreo ver , histological image data is visually observed and interpreted by pathologists by
considering the architectural characteristics, neighborhood relationships and spatial arrangements
between tissue components, and graphs ha v e prov ed to be able to quantitati v ely represent the
visual information cues acquired and processed by pathologists from histological images. Hence,
in the thesis, the author is moti v ated to e xplore fully automated handcrafted graph-based methods
for computerized analysis of histopathological images.
Another moti v ation is to explore the potential of deep learning methods in digital histopathol-
ogy . Most importantly , deep learning has recently gained popularity in v arious research domains
and achie v ed breakthrough results. But deep learning has not been explored so far for H&E
stained histopathological whole slide images of gastric cancer for the specified goals. Particu-
larly , the deep con v olutional neural networks aim to wards replacing hand-engineering of image
descriptions in traditional methods with end-to-end learning algorithms. This work attempts to
further in vestig ate this hypothesis and the o verall prospecti ve capabilities of deep learning for
the mentioned problems.
1
The terms histology , patholo gy and histopathology are interchangeably used throughout the thesis, referring to
the same underlying concept.

1.4 Contrib utions 5
1.4 Contrib utions
The study contrib utes to wards the scientific domain of histological image analysis, and can be
used for di v erse application areas in the field of digital histopathology , in the follo wing ways.
1.4.1 Scientific Contrib utions
The proposed systematic frame w ork for medical image analysis of gastric cancer images in digital
histopathology of fers no v el and constructi v e ideas, methods, experimental results, observ ations
and conclusions, which can be v aluable for the scientific community . The salient scientific
contrib utions of this work are highlighted as follo ws.
One of the most prominent scientific contrib utions includes the design, implementation and
v ariation in a no v el graph-based method called cell nuclei attrib uted r elational graph for ef fecti v e
representation and subsequent classification of histological re gions in gastric cancer whole slide
images. The graph-based approach attempts to capture and quantify the visual characteristics of
indi vidual cell nuclei, their spatial interactions with neighbors and the global tissue architecture.
Eight v ariants of the proposed cell nuclei attrib uted relational graph are concei v ed, engineered
and computationally analyzed. The hand-engineered graph-based features are quantitati v ely
e v aluated with comparisons among themselves and se v eral well-kno wn state-of-the-art feature
e xtraction methods in digital histopathology .
An original self-designed deep con volutional neural network ar c hitectur e is contrib uted after
a thorough empirical in vestig ation, to attain the defined objecti ves of medical image analysis
of gastric cancer in digital histopathology . The deep learning method follows a supervised
approach for classifying image re gions, and its performance is quantitati v ely e v aluated, sug-
gesting comparable results to handcrafted features and traditional machine learning methods.
The de v eloped architecture is studied along with its relati v e performance to AlexNet frame-
work [
Krizhe vsk y 2012
] which is e xtremely successful in general object categorization. An
ensemble of the two netw orks is further e xplored to reduce indi vidual v ariations.
The detailed e xperimental results confirm the hypothesis that a fair visual correspondence
e xists between H&E stain and HER2 immunohistochemical stain, thereby suggesting that in
general, automatic image analysis is possible in the more routinely used H&E stain based on
HER2 immunohistochemical response. Computer -based methods ha ve been able to achie ve
fa vorable results, sometimes e ven better than humans, by considering the subtle details which
are usually not clearly distinguishable by the human visual system.
The other scientific contrib utions worth mentioning include the de velopment of suitable
approaches for necrosis detection, cell nuclei classification, multiresolution combination of
visual information for segmentation enhancement, and a comprehensi ve comparati ve scheme for
the purpose of performance e v aluation.

6 CHAPTER 1. Introduction
1.4.2 A pplication Ar eas
The de v eloped methods can potentially be adapted to the follo wing application areas for practical
usability . These ha v e been briefly introduced belo w , and demonstrated in detail in Section 6.5
and Section 7.5 respecti v ely .
1.4.2.1 A utomatic Necr osis Detection
While e xamining the whole slide gastric cancer image datasets, medical e xperts ha ve disco v ered
that some of the images contain small necrotic areas. These re gions must be excluded before
proceeding with the image analysis of cancer re gions, as these act as noisy areas and interfere
with the a v ailable useful information. As a result, an automatic necrosis detection application is
de v eloped for this purpose. This method is performance and time ef ficient, and can be potentially
utilized for detecting necrosis in other tissue types.
1.4.2.2 A utomatic Determination of Histological T issue Composition
The process for multiresolution se gmentation enhancement includes an automatic cell nuclei
classification stage. This stage can be used independently for determining the types of cell
nuclei in tissue re gions, and assist in rev ealing histological composition in gastric cancer for
heterogeneous datasets. Such tissue composition measurements can possibly assist pathologists
in computer -aided diagnosis by providing a basis for automatic dif ferentiation between tumor
and non-tumor compartments of the tissue to resolv e the cancer type, grade or e xtent. So, the
tissue compositions determined in this way ha ve been used later for the analysis of cancer re gions
in this work.
1.4.2.3 Computer -aided Diagnosis: Immunohistochemistry-based Cancer Classification
Immunohistochemical staining is used in practice by pathologists to determine e xtent of malig-
nanc y , ho we v er , it is laborious to visually discriminate the corresponding malignancy le vels in
the more commonly used H&E stain. This computer -based application attempts to solve the
problem that may seem manually tedious by classifying unlabeled data in lar ge-sized whole
slide images. It can assist pathologists in diagnosis and prognosis, reducing preparation and
inspection times as well as inter and intra-observ er v ariabilities.
1.4.2.4 Content-based Image Retrie val
Suitable hand-engineered representations of tissue images can be further utilized for content-
based image retrie v al (CBIR) of desirable image re gions from the database ha ving visual and
semantic contents similar to a query image. Based on this premise, an introductory CBIR
application has been de v eloped and tested for the a v ailable gastric cancer datasets in this w ork.

1.5 Challenges in Study 7
1.5 Challenges in Study
As discussed before, computer -based analysis of histological images is being acti v ely e xplored
worldwide. Ho we v er , complete dissemination of computerized methods in practical or clinical
en vironments is still a prospecti ve challenge, mainly due to lo wer reliability compared to
pathologists’ v erdicts. Pathologists’ e xperience and e xpertise ha ve been acquired through a
specialized long-term training process, and they diagnose di v erse cases on day-to-day basis.
Along with assessment of visual appearance of tissues, pathologists also consider additional
clinical information such as patient history and etiological agents, which makes the decision-
making process more complicated and dif ficult to formalize. Also, the image analysis software
de v eloped till date is specific to certain pathological disorders, and not able to handle exceptional
cases. In general, the extent of usability and deployment of image analysis applications in a
clinical setting is a challenge for the technical de v elopers. Ne vertheless, these can be used
as bases for further scientific research to optimally achie v e the desired objecti v es. Additional
considerations before computer -based systems are adopted into routine diagnostics include the
size of the programs, ease of use, rob ustness, time requirements and data storage required for
successful application. Strict regulations and financial issues add to the challenges, making their
routine adoption dif ficult. The re gulatory standards and guidelines are expected to become more
distinct and unambiguous in future adv ancements in the field of digital pathology . Ho we v er , ev en
at present, such computer -based methods can assist medical professionals by automating se v eral
less serious tasks, e.g . providing additional opinions to pathologists, bio-banking, archi ving,
content-based image retrie v al and so on.
The image acquisition process is not ideal in most cases, hence the quality of the acquired
digital images is also a current question of discussion among pathologists worldwide. During
the process of image acquisition, se v eral problems may occur such as non-uniform cutting and
staining of tissue specimens, touching or o verlapping of samples, unclear separation, existence
of stain artifacts and inhomogeneity in the tissues. As a result, se gmentation of the tissue in
order to identify e xact boundaries of its components may become complex and suboptimal. This
ef fect is also observ ed in this research, and was o v ercome by e xploring dif ferent ways (such as
re-scanning slides with dif ferent scanners and designing a multiresolution combination approach)
to impro ve the results of a suitable se gmentation algorithm. The methods ha ve been tailored for
the specific whole slide image datasets of gastric cancer in order to address the issues related to a
non-ideal acquisition process.
Medical image interpretation is a comple x and poorly understood process. It requires
a consistently acti v e communication channel between the computer vision researchers and
the physicians for proper understanding of the images, so that it is ensured that the research
is streamlined in the correct direction and is able to deli v er as promised. This interaction
requirement pro ves a bottleneck mainly due to strict time schedules on both sides. Creating or
re vie wing of ground truth data is performed by pathologists which is manually demanding and
time-consuming in high resolution images, so acquiring medical expert kno wledge is one of

8 CHAPTER 1. Introduction
the most challenging aspects of this study . Considering the complexity in v olv ed into acquiring
and annotating histological image data, our method is currently limited to e xperimentation
with the described gastric cancer whole slide image datasets. Moreov er , reproducibility of
de v eloped methods is demanding due to the e xisting v ariablilities at se v eral le v els. F or the
same tissue type, these include biological heterogeneity , v arying tissue thickness, dif ferent
scanner properties and stain v ariations during the acquisition process. Most of these f actors are
considered in this research and also require to be addressed for future e xtensions. The usability
of de v eloped methods can further be extended for multiple tissue types, ho we ver , the challenge
will be enhanced and is a future prospect of this study .
In general, there do not exist common public benchmark databases for man y typical patho-
logical problems that are are highly specific to certain populations o wing to se v eral factors lik e
geographical location, lifestyle, gender and biological agents. The study and analysis of the
patterns, causes and ef fects of health and disease conditions in a defined population are subject
of a separate branch of science called Epidemiology [
Morabia 2013
]. As a result, a v ailability of
lar ge-scale disease-specific data is limited by such factors. Moreov er , the situation is complicated
by le gal restrictions such as access permissions and ethical issues for usage and distrib ution
of human data in the existing datasets or their e xpansion. Access to most histopathological
datasets is restricted lar gely to the org anizations holding their license, and patient data is either
pseudon ymized and (or) proper permission is sought for research purpose. The author and
associated research groups ha v e acquired and analyzed the a v ailable whole slide image datasets
according to the ethical and usage guidelines prescribed by the source of the gastric cancer
histopathological data [ W arneke 2013 ].
1.6 Organization of Thesis
The thesis is or ganized as follo ws. Chapter 2 presents a theoretical background about the v arious
methods e xplored in digital histopathology , including the domain description, widely kno wn
state-of-the-art feature e xtraction techniques and machine learning methods, which are studied
and applied for comparati v e e v aluations in the ne xt chapters. Chapter 3 emphasizes the related
work in literature for the in v estigated methods in the field of digital histopathology . Chapter 4
describes a brief o vervie w of the proposed scientific frame work sho wing the process pipeline of
the conducted e xperiments. Chapters 5 , 6 and 7 explain in detail, the design, implementation,
analysis and implication of the explored methods in the three main stages of the e xperimental
frame w ork. In Chapter 8 , the quantitativ e e v aluation of performance of the proposed techniques
along with comparati v e analysis with the established methods demonstrates the accomplishments
and scrutinizes the shortcomings and challenges in experimentation. Chapter 9 discusses the
statistical aspects in datasets in reference to the stains and its consequences. Chapter 10 summa-
rizes the studies by concluding the findings, suggesting the important prospecti v e directions of
research and pro viding useful recommendations in the field of digital histopathology .

C H A P T E R 2
Theor etical Backgr ound
Contents
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Domain Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.1 Ov ervie w of Digital Histopathology . . . . . . . . . . . . . . . . . . 10
2.2.2 Introduction to Gastric Histopathology . . . . . . . . . . . . . . . . 12
2.3 F eatur e Extraction in Digital Histopathology . . . . . . . . . . . . . . . . 14
2.3.1 Lo w-le v el (Pixel-based) Methods . . . . . . . . . . . . . . . . . . . 15
2.3.2 Object-le v el Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.3 High-le v el (Architectural) Methods . . . . . . . . . . . . . . . . . . 22
2.4 Machine Learning in Digital Histopathology . . . . . . . . . . . . . . . . 33
2.4.1 Support V ector Machines . . . . . . . . . . . . . . . . . . . . . . . . 34
2.4.2 AdaBoost Ensemble Learning . . . . . . . . . . . . . . . . . . . . . 34
2.4.3 Random Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.4.4 Deep Con volutional Neural Networks . . . . . . . . . . . . . . . . . 35
2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.1 Intr oduction
In this chapter , firstly the important principles of digital histopathology are described, including
an o vervie w of the preparation and image acquisition procedures and a background of the gastric
tissue. Ne xt, the most widely applied feature extraction and image description methods in
digital histopathology are introduced. A part of these methods describing the state-of-the-art
high-le v el (architectural) feature extraction in Section 2.3.3 were published by the author in a
re vie w article [
Sharma 2015a
]. Finally , the machine learning algorithms e xplored in the study
are briefly discussed.
9

10 CHAPTER 2. Theoretical Background
2.2 Domain Description
2.2.1 Over view of Digital Histopathology
2.2.1.1 Intr oduction
Histology , a compound of the Greek words histo -tissue and lo gos -study , is the branch of science
comprising the study of microscopic structures and functions of tissues of plants and animals.
P athology is another compound of the Greek words patho -suf fering and logos -study , relating
to the causal study of diseases. The term histopatholo gy is deri v ed from the fusion of the
branches histology and pathology , and defined as the study of microscopic diseased tissues,
where pathologists e xamine tissue specimens and provide diagnostic inferences based on their
professional medical kno wledge, e xperience and observ ations. Histopathology lays do wn the
scientific foundation for disease diagnosis, prognosis, clinical decision-making, research and
education in medicine and biology .
2.2.1.2 T issue Specimen Pr eparation
Microscopic analysis of tissue specimens requires their preparation using a comple x sample prepa-
ration procedure for subsequent observ ation of tissue components [
Hunter 1972
], [
Rolls 2011
].
A schematic o vervie w of the general process of tissue specimen preparation is illustrated in
Figure 2.1 . Initially , a tissue specimen is e xtracted from the corresponding body part of the
subject. The tissue specimen submitted for pathological analysis can be broadly classified into
two main cate gories, namely , biopsy and sur gical resection.
1. Biopsy:
A biopsy [
Zerbino 1993
] is a small tissue sample acquired mostly for the purpose of
rendering a definiti v e diagnosis.
2. Surgical r esection:
This type of tissue specimen [
Tjandra 2006
] is obtained by the therapeu-
tic sur gical remov al of the whole disease-af fected tissue region or or gan(s). Such a specimen
is usually acquired in order to pro vide definiti v e treatment of a disease when the diagnosis is
already kno wn or strongly suspected.
Fig. 2.1 A schematic o vervie w of tissue specimen pr eparation

2.2 Domain Description 11
After acquiring the tissue specimen from the or ganism’ s body , it is required to be chemically
fix ed to pre v ent decay and preserve the constituent cells. The most popular fixing agent is
formaldehyde, usually in the form of a phosphate-b uf fered solution, often referred as formalin .
F ollo wing the fixation, the tissue specimens are processed in automated instruments called tissue
pr ocessors , which allo w the specimens to be infiltrated with a sequence of dif ferent solvents
finishing in molten paraf fin w ax to provide support during cutting. Embedding procedure results
in a tissue block ready for section cutting. Sections are cut on a precision instrument called
micr otome using e xtremely fine steel blades at a thickness of 3 - 5
µ
m, ensuring that only a
single layer of cells makes up the section. This step produces v ery thin, high quality sections
called slices . The slices are then mounted on glass slides and appropriately stained in order to
impro ve contrast of specific tissue components, demonstrating normal and abnormal structures.
A professional visually inspects the sections under an optical microscope. Moreo ver , a set of
digital images called whole slide images can be acquired to be further processed or analyzed in
digital pathology .
2.2.1.3 V irtual Microscopy and Whole Slide Imaging
Recent adv ancements in technology ha v e led to the de v elopment of a set of modern methods
called virtual micr oscopy [ W einstein 2009 ] including whole slide imaging, that incorporate the
use of technical equipment such as whole slide scanners and high-performance computers to
facilitate digitization of glass slides. The resulting whole slide images (WSI) usually ha ve
Gigapix el resolutions. V irtual microscopy methods also aid in digital vie wing at se veral magnifi-
cations, storage and transmission of histological WSI o ver computer netw orks. Hence, virtual
microscopy can be seen as an inte gration of optical microscopy and digital technologies, and an
adv anced alternati v e to e xamining glass slides under the light microscope. The general virtual
microscopy pipeline including whole slide imaging is sho wn in Figure 2.2 . The procedure for
histological image analysis usually starts with the acquisition and labeling of the digital whole
slide images using virtual microscopy techniques.
Fig. 2.2 Pipeline of virtual micr oscopy . Adapted fr om [ Sae g er 2009 ].

12 CHAPTER 2. Theoretical Background
The virtual microscope functions in similar way to optical microscope. Magnification and
resolution are the two main characteristics to be considered in this re gard. Magnification is
the ability to make the objects appear lar ger , and measured in terms of magnifying po wer
(represented by a number follo wed by
×
), which denotes ho w man y times the object is magnified.
The con ventional notation in optical microscop y for compound microscopes uses only the
magnification pro vided by the objecti v e lens, i.e. v arying with each instrument, and usually a
fix ed magnification of the eye pi ece i.e. 10
×
. For e xample, a magnification of
a ×
means that
the optical lens magnifies the object
a
times, follo wed by a
10
times magnification of e ye piece,
leading to a final ef fecti v e
10 a
magnification of the object. Such notation is extended to virtual
microscopes, and also considered in this work. Resolution is the ability to distinguish between
two points and determines the amount of details that can be observ ed. It measured in terms
of resolving po wer as the smallest distance between objects that can be distinguished. For the
human e ye, it is around 0.1 mm. For digitized microscopic images, it is usually measured in
µm/pixel
representing the distance resolv ed by each pixel, thus, smaller the quantity , higher is
the resolving po wer . This notation has been used later in the thesis. Usually , the WSI scanner
resolution is suf ficient to present all rele vant magnifications on the monitor , and the size or
scale of the physiological structures to be vie wed in the slide determines the choice of working
magnification of histological images to be analyzed.
2.2.2 Intr oduction to Gastric Histopathology
2.2.2.1 Anatomy of the Stomach
The stomach is a muscular or gan situated in the left upper abdominal side of the body that
secretes acids and enzymes for digesting food. Anatomically , the stomach is di vided into four
sections [
Moore 2011
], each of which has dif ferent types of cells and functions. The sections
are Car dia where the contents of the esophagus empty into the stomach, Fundus formed by the
upper curv ature, Body or Corpus is the main central region and Pylorus is the lo wer section
that empties the contents into the small intestine via duodenum. The stomach wall is folded
into Rugae to pro vide higher absorption area. Histologically , the wall consists of four layers of
lining [ Sorenson 2008 ] as the follo wing.
1.
Mucosa: It consists of the epithelium and the lamina propria (loose connecti v e tissue), with a
thin smooth muscle layer called muscularis mucosa which separates it from the ne xt layer .
2. Submucosa: It is the ne xt layer after mucosa and comprises of the fibrous connecti v e tissue.
3.
Muscularis Externa: It comprises of the muscular layers which lie beneath the submucosa. It
is unique from other or gans of the gastrointestinal tract and consists of three layers, namely ,
the inner oblique layer , the middle circular layer and the outer longitudinal layer .
4. Ser osa: It consists of layers of the connecti v e tissue continuous with the peritoneum.
The columnar epithelium of the mucosa forms in v aginations called gastric pits [
Sorenson 2008
].
The lamina propria contains gastric glands opening into the bases of the gastric pits, which

2.2 Domain Description 13
Fig. 2.3 Pictorial r epr esentation of the stomac h. Sour ce: [ W ikipedia 2017 ]
produce and secrete gastric juices. Dif ferent types of cells are found at dif ferent parts of these
glands. A pictorial representation of stomach is sho wn in Figure 2.3 .
2.2.2.2 Gastric Car cinoma
Gastric cancer is a leading cancer type that can be subdi vided into dif ferent gastric malignan-
cies. Among these, gastric adenocarcinoma is the most common gastric malignanc y (90-95%),
follo wed by lymphomas (1-5%), gastrointestinal stromal tumor (2%), carcinoids (1%), adenoa-
canthomas (1%) and squamous cell carcinomas (1%) [
DeV ita 2010
]. Histopathologically , gastric
carcinoma is classified according to Lauren classification [
Lauren 1965
] into intestinal type and
dif fuse type.
Gastric adenocar cinoma is the stomach malignancy studied in this w ork. It is defined as a
malignant epithelial tumor that originates from glandular epithelial tissue in the mucosa layer of
the stomach, and aggressi vely spreads by infiltrating the submucosa and muscularis e xterna of the
gastric w all. Gastric adenocarcinoma can be subclassified according to histological description of
the predominant structures as tub ular , papillary , mucinous and poorly cohesi v e [ Bosman 2010 ].
2.2.2.3 Associated Staining Methods
Staining is a technique that can be used to better visualize cells and other tissue components under
a microscope. By using dif ferent stains, one can preferentially stain certain cellular components,
such as a nucleus or a cell wall, or the entire cells. Some commonly used stains for this purpose
are Haematoxylin & Eosin (H&E), Feulgen, Schif f, Wright, Ki-67 protein, T oluidine blue and
immunohistochemical stains [
P awlina 2006
]. The author describes two of the man y staining
techniques, namely , HER2 immunohistochemical stain and H&E stain, because these two stains
ha v e been applied in the whole slide image data of the subsequent experiments.
Immunohistochemical (IHC) staining (or immunostaining) is a group of staining methods
used in histopathological diagnostics of cancer [
Ramos-V ara 2014
]. An e xample of immunos-
taining is the HER2 staining. The human epidermal growth f actor receptor 2 (Her2) gene is
a proto-oncogene whose high amplification causes a protein o vere xpression in cell membrane

14 CHAPTER 2. Theoretical Background
of a malignant cell, leading to abnormal cell division and gro wth [
Chua 2012
]. It has been
most widely studied in breast cancers. Neu is a protein which is encoded by the Her2 gene in
humans. Her2/neu (or simply HER2) has been recently introduced as a predictiv e biomark er
for the treatment of gastric cancer with trastuzumab . T rastuzumab is an antibody tar geting
HER2 and is applied in combination with chemotherapy for the treatment of HER2 positi v e
adv anced gastric cancer [
W arneke 2013
]. The HER2 status is assessed by pathologists using
tumor tissue obtained by biopsy or sur gical resection and immunohistochemistry along with
in-situ hybridization. A gastric cancer is HER2 positi ve when a strong circumferential, lateral
or basolateral immunostaining is sho wn by
≥
10% of the tumor cells in the tissue, or when
weak to moderate circumferential, lateral or basolateral immunostaining is sho wn by
≥
10%
of the tumor cells in combination with HER2 gene amplification, also called the 10% cut-of f
rule [ Behrens 2015 ].
Hematoxylin-Eosin (H&E) staining is an e xample of staining method that has been used by
pathologists for o ver a hundred years because it pro vides a detailed vie w of the tissue by clearly
staining the tissue components. Hematoxylin stains the cell nucleus and other acidic structures,
such as RN A-rich portions of the cytoplasm and the matrix of hyaline cartilage with blue color .
In contrast, eosin stains the cytoplasm and collagen with pink color . Hence, the image analysis of
H&E stained images would mainly emphasize on cell nuclei, c ytoplasm and other stained tissue
component characteristics. H&E stain is the most widely used stain in histological diagnosis and
is often a basis for primary diagnosis in the field of histopathology .
As stated earlier , HER2 immunohistochemical staining is a more complex staining procedure
in v olving higher preparation costs, and is still not common in laboratory practice. Haema-
toxylin and eosin (H&E) stain, in contrast, is routinely used in histological examinations due
to clear staining of cell nuclei, easy application, lo wer preparation costs and wider usabil-
ity [
Bancroft 2008
]. Howe ver , pathologists find dif ficulty in visually dif ferentiating between
corresponding malignanc y le v els in H&E stain, the reason that H&E is used initially and HER2
immunohistochemical staining is subsequently recommended to re v eal the details of malignanc y .
Due to its adv antages, the H&E stain has been preferably analyzed in this w ork.
2.3 F eature Extraction in Digital Histopathology
In computer vision and image processing, a featur e can be defined as a set of v alues deri ved
from the higher dimensional image data that can be useful and informati v e for a gi v en task,
for e xample, image recognition, object detection and content-based image retrie v al. Features
are required to be appropriately engineered for sophisticated image analysis such as machine
learning and classification. The process of computing features from the image is known as featur e
e xtr action , and considered as a dimensionality reduction, because the salient characteristics in
the image are represented by a lo wer dimensional feature v ector . The systematic procedure to
make image data more accessible, leading to a rele v ant computer interpretation is referred as
imag e r epr esentation , and the process of quantification as imag e description . In the field of

2.3 Feature Extraction in Digital Histopathology 15
digital histopathology , the established and frequently studied handcrafted feature extraction and
image description methods are broadly di vided into three groups, namely , lo w-le v el (pix el-based)
methods, object-le v el methods and high-le v el (architectural) methods [ Gurcan 2009 ].
In this section, the three groups (and contained methods) considered as state-of-the-art for
e xtracting suitable features from tissue images in digital histopathology are briefly introduced.
A subset of these methods has been applied in our e xperiments as a baseline for quantitati v e
comparisons with the proposed methods and also during the image pre-analysis stage.
2.3.1 Low-le v el (Pixel-based) Methods
These methods incorporate detection of features through indi vidual pix els in the image, and do
not include morphological or architectural properties of tissue components. Research in this
area mainly includes approaches for e xtracting meaningful information in the form of te xture,
intensity , color and frequency-based features directly from the image pix els. In literature, these
methods ha v e been applied on v arious types of histological images for se v eral applications like
cancer classification, grading, tissue classification and content-based retrie v al, discussed in detail
in Section 3.2.1 .
2.3.1.1 T extur e Descriptors
T e xture is the most frequently used tissue characteristic in histopathological image analysis.
This is primarily because the working magnification in histological WSI (5
×
to 40
×
objecti v e
magnification) makes it possible to distinguish the v ariations in histological components purely
on the basis of te xtural dif ferences. Some of the most popular te xture descriptors are explained
as follo ws.
Gray Le vel Co-occurr ence Matrix Statistics
The commonly used features in this group consist of statistics [
Haralick 1973
] computed after
deri ving a gray le v el co-occurrence matrix (GLCM) for each image. The GLCM is used for
a series of second order statistical calculations, measuring the relationship between the gray
le v els of neighboring pix els in the original image. The GLCM is denoted by
G
, with dimension
N g × N g
, where
N g
is the number of gray le v els in the image. Each element of
G
is computed by
counting the number of times
N i,j
a pix el with v alue
i
is adjacent to a pix el with v alue
j
and then
di viding the entire matrix by the total number of such comparisons. Each entry is considered
as the probability that a pix el with v alue
i
will be found adjacent to a pix el of v alue
j
.
p [ i, j ]
is
gi v en in equation 2.1 as the probability of co-occurrence of pix els with v alues i and j .
p [ i, j ] = N i,j
P N g − 1
j =0 P N g − 1
i =0 N i,j
(2.1)
The common statistical features extracted from the GLCM are defined in T a-
ble 2.1 [ Haralick 1973 ].

16 CHAPTER 2. Theoretical Background
T able 2.1 GLCM statistical descriptors
Statistical quantity Expr ession Remarks
Angular Second Moment P i P j [ p ( i, j )] 2 Also called Ener gy or Uniformity
Contrast P N g − 1
n =0 n 2 P N g
i =1 P N g
j =1 p ( i, j ) n = | i − j |
Correlation P i P j ( ij ) p ( i,j ) − µ x µ y
σ x σ y
µ x
and
µ y
are mean v alues
σ x
and
σ y
are standard de viations of par -
tial PDFs of x , y
Entropy − P i P j p ( i, j ) l og [ p ( i, j )] –
In verse Dif ference Mo-
ment P i P j
p ( i,j )
1+( i − j ) 2
Similar to (or referred as) Homo-
geneity
Sum of Squares: V ariance
P i P j ( i − µ ) 2 p ( i, j ) –
Sum A v erage P 2 N g
i =2 ip x + y ( i )
x
and
y
are coordinates of an entry
in
G
and
p x + y ( i )
is the probability
of the co-occurence of coordinates
x + y
Sum Entropy − P 2 N g
i =2 p x + y ( i ) l og [ p x + y ( i )] –
Sum V ariance P 2 N g
i =2 ( i − S E ) 2 p x + y ( i ) SE refers to sum entropy
Dif ference Entropy − P N g − 1
i =0 p x − y ( i ) l og [ p x − y ( i )] –
Dif ference V ariance P N g − 1
i =0 i 2 p x − y ( i ) –
First Information Measure
H X Y − H X Y 1
max ( H X ,H Y )
H X Y
is Entropy ,
H X
and
H Y
are entropies of partial
PDFs
p x
,
p y
and
H X Y 1 =
− P i P j p ( i, j ) l og [ p x ( i ) p y ( j )]
Second Information Mea-
sure p 1 − e [ − 2( H X Y 2 − H X Y )] H X Y 2 =
− P i P j p x ( i ) p y ( j ) l og [ p x ( i ) p y ( j )]
Maximal Correlation Co-
ef ficient √ second lar gest eigen v alue of Q Q ( i, j ) = P k
p ( i,k ) p ( j,k )
p x ( i ) p y ( k )
Gabor Filter -bank Responses
Gabor filters are considered good approximations of the recepti v e fields of simple cells in
primary visual corte x of mammalian brain [
Mar ˇ
celja 1980
]. [
Daugman 1985
]. Gabor filter -bank
features can be selected for simulating the human visual detection abilities, as frequency and
orientation representations of Gabor filter are similar to response of human visual system. A two-
dimensional Gabor filter is a band-pass filter with impulse response
h ( x, y )
in the spatial domain
as the multiplication of two functions, a complex sinusoidal carrier
s ( x, y )
and a Gaussian
en velope g r ( x, y ) and denoted by equation 2.2 [ Bovik 1990 ], [ Mo vellan 2002 ].
h ( x, y ) = s ( x, y ) · g r ( x, y ) (2.2)
The sinusoidal carrier in spatial domain in the Cartesian coordinate system is sho wn in equa-
tion 2.3 , where U and V are spatial frequenc y and φ is the phase of the sinusoid.
s ( x, y ) = exp( j (2 π ( U x + V y ) + φ )) (2.3)

2.3 Feature Extraction in Digital Histopathology 17
It can be inferred from abo ve that,
s ( x, y )
can be represented as equation 2.4 in the polar
coordinate system, where
F = √ U 2 + V 2
is the magnitude and
ω 0 = tan − 1 ( V /U )
is the
direction.
s ( x, y ) = exp( j (2 π F ( xcosω 0 + y sinω 0 ) + φ )) (2.4)
The Gaussian en velope in spatial domain is e xpanded in equation 2.5 , where
K
is the scale of the
magnitude,
σ x
and
σ y
are asymmetric spread of the Gaussian,
x 0
and
y 0
are locations of peaks,
r
specifies rotational operation, and θ is the rotation angle.
g r ( x, y ) = K exp − 1
2 ( x − x 0 ) 2
r
σ 2
x
+ ( y − y 0 ) 2
r
σ 2
y !! (2.5)
( x − x 0 ) r = ( x − x 0 ) cosθ + ( y − y 0 ) sinθ (2.6)
( y − y 0 ) r = − ( x − x 0 ) sinθ + ( y − y 0 ) cosθ (2.7)
Example of a Gabor filter -bank consisting of twelv e two-dimensional Gabor filters with three
frequencies and four orientations is sho wn in Figure 2.4 . When an image is filtered with a set of
filter -banks at dif ferent frequencies, orientations and standard de viation combinations, their filter
responses are considered as po werful te xture discriminators [
F ogel 1989
]. The Gabor filter bank
responses can be used as ef fecti ve te xture features in histopathological image analysis.
Fig. 2.4 Example of Gabor filter -bank with varying fr equencies and orientations

18 CHAPTER 2. Theoretical Background
Local Binary Patter ns
The process of generating Local Binary Patterns (LBP) labels the pix els of an image by thresh-
olding the neighborhood of each pix el and considers the result as a binary number [
Ojala 1996
].
A LBP histogram can be computed and found ef fecti v e to represent te xtures. The LBP texture
operator has recently become popular approach for te xture classification because of high distin-
guishing potential and computational simplicity , and has been applied in se v eral applications
such as face recognition [ Ahonen 2006 ] and mo ving object detection [ Heikkilä 2006 ].
The LBP operator first di vides the image into windo ws of smaller size, and compares the
intensity of each center pix el
c
to its
p
-nearest neighbors, generating a 0 or 1 as the result of each
comparison. This operation gi v es a
p
-digit binary number corresponding to the pix el, which is
then con verted to an equi v alent decimal v alue called the LBP code. The resulting LBP image
L c,p ( x, y )
is sho wn in equation 2.8 , where
I c
is the intensity of the considered pix el and
s
is a
sign function mapping to 1 if the dif ference is greater than or equal to 0, and 0 otherwise. An
e xample of the process of generation of the LBP code is illustrated in Figure 2.5 .
L c,p ( x, y ) =
p − 1
X
k =0
2 k s ( I k − I c ) (2.8)
Fig. 2.5 The LBP code gener ated fr om an e xample imag e
The LBP image
L c,p ( x, y )
can be obtained, and its histogram for
i th
LBP code with total
N = 2 p codes is defined in equation 2.9 .
H i = X
x,y
f { L c,p ( x, y ) = i } , i = 0 , 1 , ...N − 1 (2.9)
where,
f { P }
is a conditional operator which gi v es 1 where predicate
P
is true, else 0. The
classical LBP operator uses eight nearest neighbors. As a result, for each windo w , a histogram
of the frequenc y of each number occurring can be computed as a 256-dimensional feature
v ector , and can be optionally normalized and concatenated ov er the image. Se veral e xten-
sions of LBP ha v e also been explored [
Mäenpää 2003
], such as the use of dif ferent sizes of
neighborhood [ Ojala 2002 ].

2.3 Feature Extraction in Digital Histopathology 19
V arma-Zisserman T extons
V arma-Zisserman te xture classification approach [
V arma 2002
], [
V arma 2005
] is also a popular
method for e xtracting texture features in digital histopathology . It makes use of maximum
response 8 (MR8) filters to generate filter responses of images selected randomly from each
class. The MR8 filter bank consists of 38 filters deri ved from a common Root Filter Set
(RFS), b ut only eight filter responses are used, as the filters are at multiple orientations but
their outputs are reduced by considering the maximum filter response across all orientations
for rotational in v ariance. The filters used in the V arma-Zisserman approach from the RFS are
edge filters at 3 scales with 6 orientations at each scale, bar filters at the same 3 scales with 6
orientations at each scale, a Gaussian and a Laplacian of Gaussian both with
σ
=10 which are
rotationally symmetric. The RFS filter bank generated using the open-source MA TLAB filter
implementation [ V arma 2002 ] is sho wn in Figure 2.6 .
Fig. 2.6 The RFS filter -bank to obtain MR8 filter r esponses in the V arma-Zisserman appr oach. Con-
structed using the open-sour ce MA TLAB filter implementation in [ V arma 2002 ]
.
Filter responses of sample images are aggre gated and te xton cluster centers are then collected
into a te xton dictionary using the K-means clustering algorithm [
Hartigan 1979
]. F or each image,
its filter responses are also generated and labeled with the te xton closest to it in the filter response
space. A histogram of textons sho wing the frequency of occurrence of each te xton in the image
is computed as the feature v ector representing its textural characteristics.
2.3.1.2 Color and Intensity Descriptors
Color and intensity based features play a significant role in histopathological image analysis
since dif ferent requirement-specific staining techniques are applied to stain the tissue sections in
order to highlight specific components. Approaches in v arious color spaces [
Tkal ˇ
ci ˇ
c 2003
], e .g.
RGB, HSV and L*a*b* can be utilized to e xtract features for particular re gions of interest. A fe w
commonly used color and intensity-based features in digital histopathology are described belo w .
Intensity Histogram
Intensity or gray le v el histogram is the simple histogram of pixel intensity v alues. It contains
the number of pix els in an image at each of the dif ferent intensity v alues found in that image.
F or an 8-bit grayscale image there are 256 dif ferent possible intensities, and so the histogram
will consist of 256 bins sho wing the distrib ution of pix els among those grayscale v alues. For
an
M × N
sized image
I ( x, y )
with total
L
intensity le v els, intensity histogram for
i th
intensity

20 CHAPTER 2. Theoretical Background
le v el can be defined as equation 2.10 .
H i = 1
M N X
x,y
f { I ( x, y ) = i } , i = 0 , 1 , ...L − 1 (2.10)
RGB Histogram
This histogram can be considered as the extension of intensity histogram for color images in
the RGB color space. Either individual intensity histograms of red, green and blue channels
can be considered, or a three-dimensional histogram can be produced, with the three ax es
representing the red, blue and green color channels. The component (or channel) histograms can
be concatenated to generate a single feature vector . This histogram represents the distrib ution of
colors in the image.
(a)
(b)
(c) (d)
Fig. 2.7 Example showing (a) an imag e (b) gray model and ima ge gr ay histogr am (c) RGB color model
and imag e component histogr ams (d) HSV color model and imag e component histogr ams (gener ated in
MA TLAB)

2.3 Feature Extraction in Digital Histopathology 21
HSV Histogram
HSV stands for hue, saturation and v alue, and is also alternati vely called HSB (B: brightness). It
is a common c ylindrical-coordinate representation of points in the RGB color model. Hue is the
characteristic of a visual sensation by virtue of which a stimulus is described as similar to one
of the percei v ed colors, namely , red, yello w , green and blue, or their combinations. Saturation
represents the de gree of colorfulness of a stimulus relati ve to its o wn brightness. V alue (or
brightness) is the measure of a visual sensation according to which a stimulus is considered
to emit certain de gree of light. T ransformation of an image from RGB to HSV color space
can re v eal additional details from the dif ferent color components. These color models and an
e xample image with the respecti v e intensity , RGB and HSV histograms are sho wn in Figure 2.7 .
2.3.1.3 Fr equency-based Descriptors
There are additional feature extraction methods, including the measurements in the frequenc y
domain, ho we v er , these are outside the scope of this study . Although the frequency-based features
ha v e not been widely explored in histology , the y ha v e been of interest in a fe w studies. The feature
space can be e xpanded to other domains beyond the spatial domain (where the features of pre vious
sections are computed). Basic image transforms such as Fourier transform [
Gonzales 2009
]
and W a velet transform [
Antonini 1992
] can be ef ficiently calculated from the ra w image, and
a set of the most representati v e coef ficients are selected as image descriptors. A fe w more
image transforms include the Chebyshe v transform [
Mukundan 2000
] where a set of orthogonal
polynomials are applied to approximate a smooth function, the Radon transform [
Fiddy 1985
]
projecting pix el intensities onto a radial line and the Curvelet transform [
Candes 2000
] which
is a two-dimensional e xtension of w a velet transform at multiple scales and angles. Feature
e xtraction algorithms may also be applied to the transformed image, further e xpanding the
representations in the alternate domain. The combinations of dif ferent image transforms ha ve
also been introduced as image descriptors. F or instance, image transforms and their compositions
for image classification in fluorescence microscopy images and other image types ha v e been
e xplored in [ Orlov 2008 ].
2.3.2 Object-le vel Methods
Features based on image objects are also used frequently in digital histopathology . Pathologists
usually describe object characteristics in terms of their structural appearance, where an object is
defined as a connected group of pix els satisfying a similarity criterion, for instance, cell nucleus,
v essel or gland [
Gurcan 2009
]. Fundamentally , object-lev el analysis methods depend on an
underlying se gmentation mechanism that detects the required objects in the image. Therefore,
this research direction makes use of image analysis techniques for description and representation
of constituent image objects in the re gions of interest.
Object-le v el features can be cate gorized into one of four groups, namely , morphological,
radiometric and densitometric, texture, and chromatin-specific [
Gurcan 2009
]. While the ra-

22 CHAPTER 2. Theoretical Background
diometric and densitometric, texture, and chromatin-specific features are lo w-le vel features
e xtracted from local neighborhoods, morphological measures are the most predominant and
distinct object-le v el features. The common features in this group include area, perimeter , contour
properties, bounding ellipsoid properties, con ve x hull properties, and lengths and orientations of
the ax es through the centroid [
Hufnagl 1984
]. Man y of these cell nuclei morphological metrics
ha v e been applied in the proposed methods, hence, defined later in the thesis.
2.3.3 High-le vel (Ar chitectural) Methods
The state-of-the-art pix el-based methods may be inadequate for solving certain problems in
histological image analysis since the y do not incorporate high-le vel topological information,
neighborhood relationships and tissue architecture, which are essential characteristics for distin-
guishing between healthy and abnormal tissues. The main objecti v e of this group of methods is
to model the spatial interactions and arrangements between histological objects. Segmentation
is a prerequisite for majority of these methods. Graph-based techniques are a subset of the
high-le v el methods, in volving e xtraction of discriminati v e structural features from tissues by
first constructing representati v e graphs on tissue objects. This study explores such graph-based
methods for analyzing histopathological images.
Graph is a data structure consisting of a finite set of points called nodes (or v ertices) and
a set of edges (or arcs) which link the vertices with each other based on predefined criteria.
Mathematically , a graph is a tuple
G ( V , E )
containing the set of v ertices
V
and edges
E ⊆ V × V
.
An edge in
E
connects two v ertices in
V
. A graph is planar if it can be embedded in the plane, in
other words, it can be depicted on the plane such that its edges do not cross one another e xcept
only at their endpoints, otherwise it is a non-planar graph. A graph is undirected if the edges ha v e
no orientation. A directed graph (digraph) is the one where each edge has a direction associated
with it, connecting an ordered pair of v ertices. The follo wing sections discuss some commonly
used graph-based methods in digital histopathology [ Sharma 2015a ].
2.3.3.1 V oronoi Diagram, Delaunay T riangulation and Related Graphs
The first formal definitions of Dirichlet tessellation and V oronoi diagram were proposed by
Dirichlet [
Dirichlet 1850
] and V oronoi [
V oronoi 1907
] respecti v ely . Let
V = { v 1 , v 2 ....v n }
be
a set of
n
points (or v ertices) in a plane and
d
denotes distance between two gi ven points. It is
assumed that no three points are collinear and no four points are co-circular . The planar graphs
V oronoi diagram, Delaunay triangulation and related graphs are defined using these definitions
and assumptions. These graphs are included in the lar ger group called pr oximity graphs , also
called neighborhood graphs, in which two v ertices are linked by an edge if and only specific
geometric requirements are satisfied by the vertices [
T oussaint 1991
]. Proximity graphs are
defined with reference to v arious metrics, ho we ver Euclidean metric is the most commonly used.

2.3 Feature Extraction in Digital Histopathology 23
V oronoi Diagram
Let two points (or sites)
v i
and
v j
be connected by the line se gment
v i v j
and its perpendicular
bisector
B ( v i , v j )
di vides the plane between the tw o sites into two half planes. The half plane of
site v i with respect to v j is denoted by H ( v i , v j ) and contains the set of points gi v en by ,
H ( v i , v j ) = { x | d ( v i , x ) < d ( v j , x ) } (2.11)
The V oronoi region (or V oronoi polygon)
V P ( v i )
for the site
v i
is gi v en by equation 2.12 .
V P ( v i )
is a con ve x polygon which may be unbounded.
V P ( v i ) = ∩ n
j =1 ,j  = i H ( v i , v j ) (2.12)
Hence,
V P ( v i )
contains all such points in the plane closer to site
v i
than to an y other site
v j
. The
V oronoi diagram of the set of V sites is obtained by using equation 2.13 .
V D ( V ) = ∪ n
i =1 V P ( v i ) (2.13)
V oronoi diagram performs a nearest site proximity partitioning of the plane. It is illustrated
in Figure 2.8 . Using planarity and Euler’ s formula, the V oronoi polygons always follo w the
conditions gi v en in equations 2.14 [ Rozenber g 1993 ].
Number of V oronoi polygons = n
Number of polygon edges ≤ 3 n − 6
Number of polygon v ertices ≤ 2 n − 5 (2.14)
Fig. 2.8 V or onoi Diagram of a set of r andom points
Delaunay T riangulation
The Delaunay triangulation, also called Delaunay graph, was first defined by Delaunay in
1934 [
Delaunay 1934
]. It is obtained by connecting pair of points
v i
and
v j
in the plane such
that the triangle formed by joining three non-collinear points with one side as
v i v j
is a Delaunay
triangle, and is enclosed within a circumcircle with no other point
v k ∈ V − { v i , v j }
inside
this circle, called empty cir cle property . Delaunay triangulation is depicted in Figure 2.9 .
Delaunay triangulation is the dual of V oronoi diagram, as the centroids of V oronoi polygons

24 CHAPTER 2. Theoretical Background
correspond to the v ertices in Delaunay triangulation. The duality property is also sho wn in
Figure 2.9 , and as a result of this property , the conditions in equation 2.14 are also satisfied by
Delaunay triangulation with the follo wing modification gi ven in equation 2.15 [
Rozenber g 1993
].
Subgraphs of Delaunay triangulation can be generated from Delaunay graph and e xplained ne xt.
Number of Delaunay v ertices = n
Number of Delaunay edges ≤ 3 n − 6
Number of Delaunay triangles ≤ 2 n − 5 (2.15)
Fig. 2.9 Delaunay T riangulation corr esponding to the V or onoi dia gram in F igur e 2.8 showing empty
cir cle and duality pr operties
Gabriel Graph
Gabriel graph [
Gabriel 1969
] is a subgraph of Delaunay graph and an edge exists between
v ertices
v i
and
v j
if the y are least square adjacent, i.e. if for all other v ertices
v k ∈ V − { v i , v j }
the condition gi v en in equation 2.16 is satisfied [ Matula 1980 ].
d 2 ( v i , v j ) < d 2 ( v i , v k ) + d 2 ( v j , v k ) (2.16)
Gabriel graph can be deri v ed from Delaunay graph by retaining all the edges of the graph such
that each edge is the diameter of a circle centered on the point halfway between its endpoints
that has empty circle property . Formally , line segment
v i v j
is a Gabriel graph edge for all points
v k ∈ V − { v i , v j }
if the circle with diameter
v i v j
does not contain
v k
. It is depicted in Figure 2.10 .
Fig. 2.10 Gabriel Graph corr esponding to Delaunay gr aph in F igur e 2.9 showing empty cir cle pr operty
Relativ e Neighborhood Graph
Relati v e neighborhood graph (RNG) is a subgraph of the Delaunay graph and Gabriel graph. In
this graph, two v ertices are connected by an edge if there is no other v ertex closer to both than

2.3 Feature Extraction in Digital Histopathology 25
the y are to each other [
T oussaint 1980
]. An edge exists between v ertices
v i
and
v j
if for all other
v ertices v k ∈ V − { v i , v j } the condition in equation 2.17 is satisfied.
d ( v i , v j ) ≤ max { d ( v i , v k ) , d ( v j , v k ) } (2.17)
In other words,
v i v j
is an RNG edge, if for all points
v k ∈ V − { v i , v j }
,
v i v j
is not the longest
edge of triangle
( v i , v j , v k )
. It can be deri v ed from Gabriel graph by retaining only those edges
v i v j
for which
l une ( v i , v j )
is empty , where
l une ( v i , v j )
is the intersection of two circles centered
at
v i
and
v j
, each with radius
v i v j
. RNG corresponding to the gi v en set of points along with the
empty lune property is represented in Figure 2.11 .
Fig. 2.11 Relative Neighborhood Graph corr esponding to Delaunay gr aph in F igur e 2.9 showing empty
lune pr operty
Euclidean Minimum Spanning T ree
A tree is a obtained from an undirected graph by eliminating c ycles, such that any tw o v ertices
are connected by e xactly one path. A spanning tree of a graph is a tree including all its v ertices. A
minimum spanning tree (MST) [
Graham 1985
] is the spanning tree whose sum of edge weights
is less than or equal to the sum of edge weights of e v ery other spanning tree. It is called Euclidean
minimum spanning tree (EMST) when each edge weight is the Euclidean distance between the
two v erte x-coordinates [ Czumaj 2008 ]. EMST can be deri v ed from the Relati v e Neighborhood
Graph such that edge
v i v j
is retained which is not the longest edge of a c ycle in the RNG. It is
sho wn in Figure 2.12 .
Fig. 2.12 Euclidean minimum spanning tr ee corr esponding to Delaunay gr aph in F igur e 2.9
Near est Neighbor Graph
Nearest Neighbor Graph [
Preparata 1985
] can be defined as a directed graph where
v i v j
is a
directed edge from
v i
to
v j
if for all vert ices
v k ∈ V − { v i , v j }
, the condition in equation 2.18 is
satisfied. It is depicted in Figure 2.13 for the gi v en set of points.
d ( v i , v j ) ≤ d ( v i , v k ) (2.18)

26 CHAPTER 2. Theoretical Background
Nearest Neighbor Graph, when considered as an undirected graph is a subgraph of Delaunay
triangulation and can be obtained from Euclidean minimum spanning tree by retaining the edge
for each v ertex to the closest neighbors.
Fig. 2.13 Near est Neighbor Graph corr esponding to Delaunay graph in F igur e 2.9
Se v eral algorithms e xist in literature for generation of V oronoi diagram, Delaunay triangula-
tion and its subgraphs. A summary of the popular algorithms, with their associated complexities
is gi v en in T able 2.2 , where a graph with
n
v ertices are considered. Many authors ha v e also
proposed time-ef ficient v ersions of the e xisting algorithms with improv ement in comple xities.
T able 2.2 Summary of popular algorithms for constructing V or onoi diagram, Delaunay triangulation and
its subgraphs
Graph type Associated algorithms T ime complexity
V oronoi diagram
Fortune’ s sweepline algorithm [
Fortune 1987
]
O ( nl og ( n ))
Di vide and conquer algorithm [
Shamos 1975
]
O ( nl og ( n ))
Incremental algorithm [ Green 1978 ] O ( n 2 )
Naïve algorithm [ Okabe 2009 ] O ( n 2 log ( n ))
Lloyd’ s algorithm [ Lloyd 1982 ] O ( nl og ( n ))
Delaunay triangula-
tion
Bo wyer -W atson algorithm [
Bo wyer 1981
],
[ W atson 1981 ]
O ( nl og ( n )) to O ( n 2 )
Lawson’ s flip algorithm [ Lawson 1977 ] O ( n 2 )
Lifting or projection algorithm
[ Zimmer 2005 ]
O ( nl og ( n ))
Gabriel graph From Delaunay graph [ Matula 1980 ] O ( n )
Relati v e neighbor-
hood graph
From Delaunay graph [ Lingas 1994 ] O ( n )
Euclidean min-
imum spanning
tree
From Delaunay graph [ Eppstein 1999 ] O ( nl og ( n ))
Nearest neighbor
graph
From Delaunay graph [ Edelsbrunner 1987 ] O ( nl og ( n ))
Ulam T ree
Ulam T ree is a also deri ved from the V oronoi diagram. It is a mathematical object gro wing in
space and time according to certain rules [
Ulam 1966
]. It is generated from the Delaunay graph
in such a way that the tree branches tra v erse only those V oronoi polygons which are not trav ersed
by an y other branch of the tree. Marginal polygons are not considered during tra v ersal. Ho we v er ,
there is not suf ficient theoretical e xplanation or experimental e vidence for this deriv ati v e in
pre vious literature. It is depicted in Figure 2.14 (with start point S).

2.3 Feature Extraction in Digital Histopathology 27
Fig. 2.14 A V or onoi diagram and its corr esponding Ulam T r ee
2.3.3.2 β -Skeleton
In [ Kirkpatrick 1985 ], Kirkpatrick and Radke ha ve defined a parameterized group of proximity
graphs kno wn as
β
skeletons,
β
being the parameter . The neighborhood of v ertices
v i
and
v j
is defined by
N i,j ( β )
for a fix ed
β
. The
β
skeleton is an undirected graph, where tw o vertices
v i
and
v j
are connected by an edge if and only if no other point is located in
N i,j ( β )
. A sphere
centered at point p and radius r is denoted as S ( p, r ) .
F or
0 < β ≤ 1
, v ertices
v i
and
v j
are
β
-neighbors if and only if the neighborhood defined
by the intersection of the two spheres each of radius
d ( v i , v j ) / 2 β
which pass through points
v i
and
v j
in their boundary , contains no point
v k ∈ V − { v i , v j }
. In this case, the lune-based
and circle-based neighborhoods are identical. For
1 < β < ∞
, there are two definitions for
β
skeletons, as follo ws.
Lune-based β Skeleton
Points
v i
and
v j
are lune-based
β
-neighbors if an only if the lune
N i,j ( β )
defined by the intersec-
tion of the spheres in equation 2.19 contains no other point v k ∈ V − { v i , v j } .
N i,j ( β ) = S 1 − β
2 ! v i + β
2 v j , β
2 d ( v i , v j ) !
∩ S 1 − β
2 ! v j + β
2 v i , β
2 d ( v i , v j ) ! (2.19)
Cir cle-based β Skeleton
Points
v i
and
v j
are circle-based
β
-neighbors if and only if the neighborhood defined by the union
of the two spheres each of radius
β
2 d ( v i , v j )
passing through points
v i
and
v j
in their boundary ,
contains no point v k ∈ V − { v i , v j } .
β
neighborhoods with di v erse v alues of
β
are sho wn in Figure 2.15 . It can be seen that
β
-skeletons contain both relati ve neighborhood graphs and Gabriel graphs as special cases.
Gabriel graph can be defined as lune-based and circle-based 1-skeleton (
β = 1
) and the relati v e
neighborhood graph as lune-based 2-skeleton ( β = 2 ) [ Jaromczyk 1992 ].

28 CHAPTER 2. Theoretical Background
(a) (b) (c) (d)
Fig. 2.15
β
neighborhoods (shaded r e gions) with dif fer ent values of
β
.(a) Lune-based and cir cle-based
β = 0 . 5 (b) Lune-based and cir cle-based β = 1 (c) Lune-based β = 2 (d) Cir cle-based β = 2
2.3.3.3 J ohnson-Mehl T essellation
V isualization of gro wth models is essential in man y technical processes, including histological
changes. For representing such phenomena, spatial patterns obtained from simple gro wth
processes can be used. The Johnson-Mehl model [
Johnson 1939
] is defined as a Poisson-V oronoi
gro wth model sho wing growth of particle aggre gates, where a Poisson point process is applied to
generate nuclei asynchronously , and the nuclei gro w at the same radial speed [
Anton 2009
]. The
i th
generator
P i = ( p i , t i )
is defined by a planar position v ector
p i
and associated appearance
time
t i
. The Johnson-Mehl tessellation can be vie wed as comparable to a dynamic v ersion of an
additi v ely weighted V oronoi diagram [
Anton 1998
], in which the weights indicate the associated
appearance times of particles in
R 2
[
Okabe 2009
]. In stochastic geometry , the Johnsons-Mehl
model is constructed to measure arbitrarily distrib uted geometrical properties.
The generation of Johnson-Mehl tessellation is explained in [
Anton 2009
]. After appearance
of a ne w nucleus the tessellation changes, as the incoming nucleus is inserted with a ne w V oronoi
re gion and the neighboring V oronoi polygons are changed. The sizes of the associated spheres
of nuclei are increased by the gro wth proportional to the time interv al between the pre vious
appearance and current one (
t i − t j
). This type of spatial gro wth uses a Poisson point process,
defined according to two cases of radial speed in [
Anton 2009
], namely , time homogeneous
Poisson point process and time inhomogeneous Poisson point process. An example of Johnson-
mehl tessellation for a random set of points at two time instants is sho wn in Figure 2.16 .
2.3.3.4 O’Callaghan Neighborhood Graph
An alternate definition of the neighborhood of a point manifested by O’Callaghan neighorhood
graphs in [
O’Callaghan 1975
] has been applied to histopathological images. For the histological
conte xt, O’Callaghan neighborhood graphs ha v e been discussed in detail in [
Kayser 1987
], where
v ertices are defined as structur es . T wo types of constraints apply to the neighborhood, namely
distance constraint and direction constraint. T wo structures are neighbors if the y are located
within a certain distance (distance constraint), and not hidden behind other points classified as
neighbors (direction constraint). It is sho wn in Figure 2.17 , where
v 1 , v 2 , v 3
are neighbors of
v i
,

2.3 Feature Extraction in Digital Histopathology 29
(a) (b)
Fig. 2.16 J ohnson-Mehl tessellation for a set of r andom points (a) gr owth of particles at
t = t 1
(b)
gr owth of particles at t = t 1 + 50
ho we ver
v 4
is not a neighbor of
v i
. All distances
d i 1
to
d i 3
are belo w the upper distance threshold.
Direction constraint is not fulfilled by v 4 , as v 4 is hidden behind v 2 .
Fig. 2.17 O’Callaghan neighborhood gr aph
2.3.3.5 Cell Graph
As the name suggests, cell graphs are formed by considering cells or cell clusters as vertices,
and relationships between them as edges using certain predefined linking rules. Cell graphs
are non-planar graphs as crossing of edges are allo wed. The authors introducing cell graphs
initially define the linking probabilities using the W axman model [
Gunduz 2004
]. Ho we ver , in
subsequent works lik e [
Bilgin 2007
], three dif ferent v ariations of cell graphs are defined. These
are depicted in Figure 2.14 using a v ailable histological images. Figure 2.18 (a) and Figure 2.18 (b)
are created with 400 times magnification, whereas Figure 2.18 (c) with 100 times magnification
for clarity .
Simple Cell Graph
In simple cell graph, an edge exists between gi v en two v ertices
v i
and
v j
if and only if the
Euclidean distance
d e
between the v ertices is less than a predefined threshold
D
, which is defined

30 CHAPTER 2. Theoretical Background
according to the characteristics of the tissue architecture.
d e ( v i , v j ) < D (2.20)
Pr obabilistic Cell Graph
In probabilistic cell graph, edges are assigned to vertices depending on the distance between
v ertices, by using a probability function which can be defined according to the tissue being
analyzed. One such probability function used in literature is gi v en by:
P ( v i , v j ) = d e ( v i , v j ) − α (2.21)
where
v i
and
v j
are two v ertices and
d e
denotes the Euclidean distance between them.
α
is an
e xperimental parameter which determines the rate at which probability of a link decreases with the
increase in distance between v ertices, hence, controls the graph density . The edges are defined by
a binary relation
E
on
V
for all pair of v ertices
v i
and
v j
, such that
E = { ( v i , v j ) : P ( v i , v j ) > r }
,
r
being a real number between 0 and 1. W ith such construction, the vertices closer to each other
are more likely to be link ed compared to v ertices farther a way , ho we v er it is not necessary that
a link will be formed e v en if the distance between vertices is small, and will depend on the
parameter α .
Hierar chical Cell Graph
Hierarchical graph is generated by connecting smaller subgraphs representing cell clusters
together to form the lar ger graph on the image. The clusters are identified as cell groups
e xceeding a threshold of the number of cells in a gi ven re gion, defined by placing a grid on top
of the image. Hierarchical graphs are b uilt using these clusters as v ertices, where each verte x
represents a simple cell graph on the cells included in the cluster . The same linking rules are
applied as for probabilistic cell graphs.
(a) (b) (c)
Fig. 2.18 Repr esentative cell graphs for small r e gions of H&E stained images (a) Simple cell gr aph with
D = 200
for gastric tissue r e gion (b) Pr obabilistic cell graph with
α = 2
for gastric tissue r e gion (c)
Hierar c hical cell graph for br east tissue wher e each verte x r epr esents a cell cluster with a corr esponding
simple cell graph b uilt on the constituent cells (denoted by r ectangle)

2.3 Feature Extraction in Digital Histopathology 31
2.3.3.6 Attrib uted Relational Graph
A graph
G
is an Attrib uted relational graph (ARG) when its v ertices and the edges contain
associated attrib utes. The v ertex attrib utes for
v i
are denoted as a v ector
a i = [ a ( k )
i ] , ( k =
1 , 2 , 3 , ..., K )
, where
K
is the number of v ertex attrib utes, and the edge attributes (or weights)
for
e j
by the v ector denoted as
b j = [ b ( m )
j ] , ( m = 1 , 2 , 3 , ..., M )
, where
M
is the number of edge
attrib utes [
Sharma 2012
]. In Figure 2.19 , ARG with verte x attrib ute v ectors
a i , i = 1 , 2 , 3
and
edge attrib ute v ectors b j , j = 1 , 2 , 3 is sho wn. V ertex attrib utes represent object properties like
size, position, shape and color whereas edge attrib utes define relationships between v ertices such
as the distance, common boundary and dissimilarity between objects. Linking between vertices
may be based on the selected neighborhood condition ( e .g. O’Callaghan, proximity graph).
Fig. 2.19 Attrib uted Relational Graph
2.3.3.7 Global Graph F eatur es
When the graphs ha ve been constructed for images using the abo v e described methods, the
ne xt step in image analysis is to extract appropriate features from these graphs. One way of
achie ving this is to define a set of global graph measures. There are man y graph-based metrics
and measures which can be deri v ed from graph representations. Definitions of se v eral measures
can be found in [
W allis 2010
]. Some of the most commonly used global graph features in image
analysis are described as follo ws:
1. Graph size:
Graph size represents the span and extent of a graph, and can be measured by
counting the total number of v ertices, total number of edges and total number of trees in the
graph.
2. Degr ee:
De gree of a verte x is defined as the number of neighbors of a v erte x, or number of
v ertices linked to the gi ven v ertex by edges. A v erage degree
D av
of graph
G
with
n
v ertices
and de gree
D i
of the
i th
v ertex
v i
is calculated as belo w , and suitable for representing the

32 CHAPTER 2. Theoretical Background
relati v e density or sparsity of the graph.
D av = 1
n
n
X
i =1
D i (2.22)
3. Clustering coefficient:
Clustering coef ficient is the measure of the e xtent to which v ertices
in a graph ha v e a tendency to cluster together . For a gi v en v ertex
v i
, let the neighboring
v ertices be contained in the neighborhood set N i defined as,
N i = { v j ∈ V ∀ v j  = v i , ( v i , v j ) ∈ E } (2.23)
F or an undirected graph, clustering coef ficient C i for v ertex v i is computed by ,
C i = 2 | e j k |
D i ( D i − 1) : v j , v k ∈ N i ; e j k ∈ E (2.24)
where,
D i
is the de gree of verte x
v i
and
| e j k |
is the total number of edges between the
neighbors of
v i
. It can be noted that
0 ≤ C i ≤ 1
. The a v erage clustering coef ficient
C av
for
the graph can be calculated as,
C av = 1
n
n
X
i =1
C i (2.25)
4. Eccentricity:
The eccentricity
ϵ i
of a graph verte x
v i
is the maximum distance between
v i
and an y other verte x
v j
in
V
. It can be considered as the measure of how f ar a v ertex is
from the v ertex most distant from it in the graph. The vertices of a disconnected graph are
said to ha ve infinite eccentricity [
W est 2000
]. The minimum graph eccentricity is called the
graph radius and the maximum eccentricity of graph is called the graph diameter . A verage
eccentricity of the graph can be calculated as,
ϵ av = 1
n
n
X
i =1
ϵ i (2.26)
5. Path length:
A path in a graph or tree is a finite or infinite sequence of edges which connects
a sequence of v ertices. Edges can be part of a path only once. The minimum, av erage and
maximum path lengths of the graph can be calculated. Let
d ( v i , v j )
, where
v i , v j ∈ V
denote
the shortest distance between
v i
and
v j
, assuming that
d ( v i , v j )=0
if
v i
cannot be reached
from
v j
. A verage path length
l av
for an undirected graph can be defined as [
T omassini 2010
],
l av = 2
n ( n − 1) X
i  = j
d ( v i , v j ) (2.27)
6. Connected Component Ratio:
A connected component (or just component) of an undirected
graph is a subgraph in which any tw o v ertices are connected to each other by paths, and which
is not connected to additional v ertices in the super graph. The connected component ratio
for a connected component in the super graph is defined in equation 2.28 , where
n v c
is the

2.4 Machine Learning in Digital Histopathology 33
number of v ertices in the gi v en connected component and
n
is the total number of v ertices in
the super graph.
C C R = n v c
n (2.28)
7. Cyclomatic number:
A c ycle is a path where the start verte x is also the end v erte x. Cyclo-
matic number or circuit rank
γ
of a graph is the minimum number of edges which must be
remo ved in order to eliminate all c ycles from the graph [
V olkmann 1996
]. Considering a
graph with n v ertices, n e edges and n c connected components, γ is gi v en by ,
γ = n e − n + n c (2.29)
8. Statistical descriptors:
F or V oronoi diagrams and Delaunay triangulations, there can be
certain measures such as area and perimeter associated with the indi vidual V oronoi polygons
and Delaunay triangles respecti v ely . F or ARGs, there can be se veral attrib utes associated
with v ertices and edges. Statistical descriptors can be used for globally describing these
attrib utes of indi vidual v ertices and edges. These include mean, standard de viation, minimum
to maximum ratio, disorder , ske wness, kurtosis and higher -order descriptors. F or these graphs,
co-occurrence matrix can also be constructed and statistical features [
Haralick 1973
] can also
be e xtracted from them. These statistical descriptors are capable to represent se veral di verse
properties of entire graphs and their v ariations quantitati vely .
9. Spectral descriptors:
Spectral features include properties e xtracted from the matrices used
to describe the graph, such as adjacency matrix and Laplacian matrix, and their study is
called spectral graph theory [
Brouwer 2011
]. Some of the frequently used features include
eigen v alues, spectral radius, eigen e xponent, Cheeger constant, ei genmode perimeter , eigen-
mode v olume and so on [
W ilson 2005
], [
Luo 2003
] These spectral features ha v e the ability
to indicate v arious fundamental properties of the graph.
2.4 Machine Lear ning in Digital Histopathology
Machine learning is the branch of computer science which includes de v elopment of algorithms
using which computers can learn from empirical data without the requirement of e xplicit pro-
gramming. The spectrum of machine learning methods ranges from artificial neural networks
to decision trees, ensemble learning and most recently deep learning. Man y machine learning
methods ha v e been employed for se veral image analysis applications, e.g . semantic se gmentation,
re gion classification, cell nuclei classification and content-based retrie v al in the field of digital
histopathology . Four popular machine learning methods ha ve been e xplored in this work for the
specified image analysis tasks on the gastric cancer datasets, namely , support v ector machines,
AdaBoost ensemble learning, random forests as traditional methods and con v olutional neural
networks as deep learning methods. These four methods are briefly introduced in this section,
and described later in the respecti v e sections of the thesis.

34 CHAPTER 2. Theoretical Background
2.4.1 Support V ector Machines
Support v ector machines (SVM) is a group of non-probabilistic supervised learning methods. An
SVM is a discriminati v e classifier formally defined by a separating hyperplane, i.e. , gi ven labeled
training data consisting of handcrafted features, the algorithm outputs an optimal hyperplane
which cate gorizes ne w incoming e xamples. The learning phase generates non-linear classifiers
using maximum mar gin hyperplanes after applying the kernel trick [
Boser 1992
] in order to
discriminate between non-linearly separable data. The current standard SVM algorithm was
proposed in [
Cortes 1995
], ho we ver , its v ariations ha v e been successfully applied to di v erse clas-
sification and re gression analysis tasks in pattern recognition and computer vision [
Byun 2002
].
A fe w dra wbacks of SVM include lo wer rob ustness to irrele v ant descriptors leading to the
requirement of feature selection [
Chen 2006
], and also the ov erhead of e xhausti v e parameter
selection of multiple parameters. Details of the SVM algorithm applied in this work is gi ven in
Section 6.2.4.1 .
2.4.2 AdaBoost Ensemble Lear ning
Ensemble learning [
Rokach 2010
] is a set of machine learning methods which use a collection
of classifiers or learners and obtain a joint classification decision by combining their predictions.
AdaBoost [
Freund 1995
] or adapti v e boosting is a well-kno wn ensemble learning approach that
allo ws adding a sequence of weak learners to the algorithm until a desired lo w training error is
achie v ed. The strong learner is assembled through a weighted majority v oting scheme on the
weak learners. AdaBoost is the most widely used form of boosting, as a boosting algorithm like
AdaBoost in v olving a combination of classifiers is proposed as a ne w direction for impro ving
the performance of indi vidual classifiers like kNN, Nai ve Bayes and SVM [
K otsiantis 2007
].
AdaBoost is also found to be easier to implement than other maximal margin classifiers lik e SVM.
These reasons gi v e AdaBoost an edge o ver other traditional machine learning approaches for
supervised classification, and has been e xplored in the research. Ho we v er , AdaBoost also suf fers
from some limitations such as high sensiti vity to wards noise and outliers, and high computational
requirements due to an ensemble of classifiers. The details of the specific AdaBoost algorithm is
pro vided in Section 6.4.2.2 .
2.4.3 Random F or ests
Random forest [
Breiman 2001
] is also a popular ensemble learning method which constructs
man y decision trees during the training phase. The final class is voted by the indi vidual trees
during classification. Like the SVM and AdaBoost methods, training a random forest also
requires prior computation of suitable handcrafted features from the corresponding data to
represent their characteristics. Random forests improv e the classification by indi vidual decision
trees which are weak learners through ensemble learning, and is considered as good as AdaBoost
in terms of prediction po wer , e v en sometimes better by its authors. Furthermore, it is faster than

2.4 Machine Learning in Digital Histopathology 35
bagging and boosting, simple to implement and interpret, and can be easily parallelized. It is also
described relati v ely rob ust to outliers and redundant v ariables, and is able to ef ficiently handle
lar ge databases and missing data. Therefore, random forests ha ve been selected as one of the
traditional machine learning methods, details of which are described in Section 7.4.1.3 .
2.4.4 Deep Con v olutional Neural Networks
Deep learning is a subset of a lar ger group of machine learning methods, which comprises
of algorithms with hierarchical processing layers performing non-linear transformations to
represent and learn data characteristics ef fecti vely [
LeCun 2015
]. Deep learning methods are
currently being e xplored in v arious fields such as computer vision, audio and speech processing,
natural language processing, information retrie v al and bioinformatics [
Deng 2014
]. Se v eral deep
learning architectures, for example, con v olutional neural networks, deep belief networks and
recurrent neural networks ha ve been introduced [
Deng 2012
], and ha v e reported to achie v e state-
of-the-art results in a number of tasks. The goodness of data representation notably af fects the
performance of machine learning algorithms. Specifically , the existence of lar ge-scale data has
been recognized as a prerequisite for the success of majority of deep learning applications, leading
to a con ver gence between the fields of deep learning and big data analytics [ Najafabadi 2015 ].
One of the most frequently used methods of deep learning for two- dimensional data is
the deep con volutional neural networks (CNN). These netw orks consist of interconnections
emulating the arrangements in visual corte x in animals, like indi vidual neurons are organized in
a manner to respond to the o verlapped tessellations comprising the visual field [
Hinton 2005
].
The principle transformations in deep CNNs mainly consist of combinations of con v olutional,
pooling and fully connected layers, and their parameters are trained using backpropagation
through these layers [
Cire gan 2012
]. Deep learning using con v olutional neural netw orks does
not necessarily require prior computation of handcrafted features, and directly processes raw
input images to compute self-deri v ed and learned features during the training process. In recent
years, CNNs hav e achie ved breakthrough performance due to the a v ailability of lar ge-scale
training data and huge parallelization using graphics processing units (GPU) to speed up the
application i.e . training and deployment process [
Strigl 2010
]. This has led to de velopment of
a number of time-ef ficient GPU-based frame works [
Jia 2014
] for deep learning which can be
applied for v arious image analysis tasks. All these recent adv ancements ha v e led to the choice of
deep learning to e xplore their potential for image analysis in digital histopathology .
The abo ve machine learning methods ha v e been selected due to mutually distinct and unique
properties, for instance, traditional machine learning v ersus deep learning, single classifiers ver -
sus ensemble of classifiers, probabilistic versus deterministic algorithms, and dif ferent paradigms
in v olved in learning such as mar gin maximization, boosting, decision trees or neural networks,
that can facilitate an e xhausti ve comparison, along with corresponding observ ations and conclu-
sions in the undertaken study .

36 CHAPTER 2. Theoretical Background
2.5 Summary
This chapter pro vides the description of related medical concepts in the histopathological domain,
theoretical background of the state-of-the-art and widely kno wn image analysis techniques
including feature e xtraction and machine learning in digital pathology . The useful methods
related to this work ha ve been introduced and defined in the chapter , unless otherwise described
later in the corresponding sections of the thesis.

C H A P T E R 3
Related W ork in Digital Histopathology
Contents
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2 F eatur e Extraction in Digital Histopathology . . . . . . . . . . . . . . . . 38
3.2.1 Lo w-le v el (Pixel-based) Methods . . . . . . . . . . . . . . . . . . . 38
3.2.2 Object-le v el Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2.3 High-le v el (Architectural) Methods . . . . . . . . . . . . . . . . . . 40
3.3 Machine Learning in Digital Histopathology . . . . . . . . . . . . . . . . 43
3.3.1 Support V ector Machines . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3.2 AdaBoost Ensemble Learning . . . . . . . . . . . . . . . . . . . . . 44
3.3.3 Random Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.3.4 Deep Con volutional Neural Networks . . . . . . . . . . . . . . . . . 44
3.4 Image Analysis in Gastric Cancer . . . . . . . . . . . . . . . . . . . . . . 45
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.1 Intr oduction
This chapter discusses the recent studies in literature that are related to the w ork demonstrated in
this research, for image analysis in the field of digital histopathology . These are org anized in the
sequence starting with the feature extraction methods follo wed by the machine learning methods
and finally the research related to analysis of gastric cancer . A section of the literature re vie w ,
especially relating to the feature e xtraction methods, was published by the author in a re vie w
article [ Sharma 2015a ] during her studies.
37

38 CHAPTER 3. Related W ork in Digital Histopathology
3.2 F eature Extraction in Digital Histopathology
3.2.1 Low-le v el (Pixel-based) Methods
Pre vious literature in this area mainly includes approaches for e xtracting information in the form
of te xtural, color and intensity , and frequency-based features, for example, GLCM statistics,
Gabor filter -banks, LBP histograms, V arma-Zisserman textons, color and intensity measurements.
These methods ha v e been applied on se v eral types of histological images for applications such
as cancer classification, tumor grading, tissue cate gorization and content-based image retrie v al.
T exture has been recognized as a highly significant group of lo w-le vel features to characterize
histological images. GLCM texture features are e xtracted in [
W alker 1994
] to determine if cervi-
cal cell nuclei are normal or abnormal. T exture analysis using optical density , GLCM and gray
le v el run-lengths is used in [
Hamilton 1997
] to classify colorectal mucosa images into normal and
adenomatous (dysplastic). Studies described in [
Shuttle w orth 2002a
] and [
Shuttle w orth 2002b
]
use te xture features from co-occurrence matrices in distinct color spaces for classifying colon
cancer images. In [
Zhao 2005
], Gabor filter -bank based texture features are applied to clas-
sify histological images into dif ferent tissue types depending on their or gans or parts of body .
Another study using Gabor filters is presented in [
Y u 2008
] for analyzing semantic content in
gastrointestinal tract histological images. GLCM along with Gabor filter-bank responses are
used for feature e xtraction in [
Thomas 2008
], a prototype method to transform the appearance of
irre gular tissues to frieze-like patterns in one dimension. Local binary pattern texture features are
applied for automated identification of epithelium and stroma in tissue microarrays of colorectal
cancer in [
Linder 2012
]. Local binary patterns are also explored for stromal area remo v al in
Ki-67 stained histological images in [
Alomari 2012
]. Co-occurrence features along with local
binary patterns are used for classifying neuroblastoma images in [
Sertel 2009
]. V arma-Zisserman
te xton approach has been used in histological image analysis tasks and performed well such
as in [
Khurd 2010
] for automatic Gleason grading of H&E images of prostate cancer . Se veral
filter -banks such as Gabor and Maximum Response filters used in the V arma-Zisserman approach
ha v e also been applied in a similar texton histogram approach for classification in breast cancer
tissue microarray samples in [ Y ang 2009 ].
Color and intensity-based features ha v e been widely explored in digital pathology . Early
works include the computation of gray-le vel features in [
W ong 1983
] for parallel classification
of muscle tissue images. More recently , analysis of stain components has been performed using
color -based SIFT features in H&E stained skin biopsy [
Díaz 2010
]. Histological specimens
stained with H&E are se gmented in original color space using a supervised color normalization
and classification scheme in [
K othari 2011
]. In additional related works, color and intensity
features are used in conjunction with the other lo w-le vel feature groups (mentioned later under
hybrid approaches).
Frequenc y-based features include the deployment of spectral bands, F ourier transforms
and wa velets among others. Applications utilizing multiple spectral bands for analysis in-

3.2 Feature Extraction in Digital Histopathology 39
clude [
Roula 2002
] and [
Rajpoot 2003
]. W a velets ha ve also been pro ved as po werful lo w-le v el
features e .g. in [
De W ouwer 2000
], [
Jafari-Khouzani 2003
] and [
Qureshi 2008
] for breast can-
cer , prostate cancer and meningioma classification respecti v ely .
Hybrid methods with dif ferent combinations of lo w-lev el features ha ve also been e xplored in
the past. Integration of te xture and intensity-based features has been popularly used in dif ferent
applications. For e xample, GLCM features are studied in combination with intensity-based
features in [
Esgiar 1998
] for classifying colon mucosa as normal or cancerous. In [
Doyle 2006
],
te xture features obtained from co-occurrence matrices and Gabor filters are used with intensity-
based features for detecting prostatic adenocarcinoma. Other approaches using combination
of te xture and intensity-based features include [
W iltgen 2003
] and [
K ong 2009
] for analysing
melanoma and neuroblastoma respecti v ely . A work combining frequenc y-based features with
te xture properties can be found in [
Orlo v 2008
]. A hybrid lo w-lev el method proposed as a part
of this research in [ Sharma 2016 ] combines te xture and color features.
Content-based retrie v al of histological images has been in v estigated in pre vious literature
using lo w-le v el methods. A work for retrie v al of skin cancer images using te xture features is
described in [
Chung 2001
]. A CBIR system for retrie ving visually similar and pathologically
rele v ant images from a database of cervicographic specimens has been proposed in [
Xue 2007
],
where color features, texture features, size of lesion and spatial location features are first extracted
from the image re gions, and distance measures are defined to establish similarity to a query region
marked by the user . In [
Caicedo 2011
], the authors perform retrie v al of basal-cell carcinoma
images using an e xample image as query , where a set of global te xture and intensity features is
applied, follo wed by a k ernel-based semantic image annotation strategy , facilitating automatic
annotation of images in lar ge collections. A semi-automatic method using a combination of
lo w-le vel features, namely , color , intensity , orientation and texture from tissue images is proposed
in [
Romo 2011
] for automatic selection of re gions of interest for further diagnosis. A multi-
tiered CBIR system for microscopic images of se v eral tissues (follicular lymphoma slides with
three sub-types and neuroblastoma slides with four sub-types) is described in [
Akakin 2012
],
where the authors e xtract color and texture features and perform slide-le vel image retrie v al using
multi-image query .
3.2.2 Object-le vel Methods
This group of methods predominantly includes the e xtraction of shape-based or morphological
features of image objects. These features ha v e been e xtensi v ely studied in histological image
analysis. The dissertation [
Hufnagl 1984
] e xplores the mathematical modeling of cell nuclei
in histological images using object-le v el metrics, which has b uilt the premise for an essential
part of this research. Moreo ver , in [
Hamilton 1987
], H&E stained colorectal epithelium images
are analyzed using morphological features follo wed by discriminant analysis that classifies
images as normal and malignant. In [
Hamilton 1994
], cytometric features based on cell mor -
phology are used for classifying fine needle aspiration cytology breast specimens into benign
and malignant by constructing Bayesian belief networks. Anderson et al. [
Anderson 1997
]

40 CHAPTER 3. Related W ork in Digital Histopathology
use morphological features of glandular tissue components (ducts and lumens) to discriminate
breast tissue images into ductal hyperplasia and ductal carcinoma in situ. In [
F arjam 2007
],
morphological features are e xtracted to classify prostate biopsy samples into benign and ma-
lignant. An application called F atFind [
Goode 2007
] considers the property of perfect round
shape of adipoc ytes for their automatic counting. Other morphological studies in histology in-
clude [
Deligdisch 1993
], [
Thiran 1996
] and [
Nedzv ed 2007
] for analyzing o v arian cancer , lungs
and digesti v e tract, and multiple kinds of tumors respecti v ely .
Se v eral studies also exhibit a fusion of the lo w-le vel and object-le vel methods. Papanico-
laou smears of cervical epithelial lesions are analyzed using te xture, color and shape features
in [
Stenkvist 1987
]. An application of CBIR with histopathological images using color and
morphological characteristics and property concept frame representation based on fuzzy logic is
described in [
Jaulent 2000
]. A combination of te xture and morphological features is e xplored
in [
Spyridonos 2001
] for bladder carcinoma, and morphological and colorimetric features are
combined in [
Zhou 2002
] for the analysis of lung cancer images. T issue composition in prostate
neoplasia using GLCM-based te xture and morphology is determined in [
Diamond 2004
]. A
multiresolution approach de v eloped as a part of this work also uses object-le vel te xture, intensity
and morphological features for automatic classification of cell nuclei of gastric cancer tissue
in [ Sharma 2015b ].
3.2.3 High-le vel (Ar chitectural) Methods
Graphs constitute an interesting area of research for analysis of histological image data, due to
their ability to ef ficiently represent tissue architecture in suitable w ays. High-le vel or architectural
feature e xtraction methods are being increasingly de v eloped to characterize tissues using spatial
distrib ution and neighborhood properties by constructing representati v e graphs and calculating
a set of appropriate local and global graph-based metrics from respecti v e image descriptions.
As stated before, a comprehensi v e literature surve y of graph-based methods for image analysis
in digital histopathology performed as part of this research is presented in [
Sharma 2015a
],
discussing the progress of e xciting de v elopments and applications in this field.
Graph-based methods for histological images were initially explored in the direction of syn-
tactic analysis. Syntactic methods based on neighborhood conditions in pattern recognition were
introduced in [
Fu 1974
]. Follo wing this, a relati vely ne w technique was introduced in diagnostic
quantitati v e pathology called syntactic structur e analysis , which was belie ved to pro vide quanti-
tati v e information on tissue architecture [
V an Diest 1995
]. The earliest work in this direction is
presented by Pre witt et al. [
Pre witt 1978
] for characterizing epithelial tissues of urinary bladder
mucosa, where measurements are performed using an interacti v e digital image processing and
decision-making system called PEEP/DECIDE/GRAPH [
Pre witt 1977
] to automate diagnostic
procedures. Features are e xtracted from haematoxylin stained cells representing intensity , texture,
shape, dif ferentiation and structure, and from tissue re gions quantifying spatial distrib utions and
tissue or ganization. Graphs and grammars in histology are formally defined in [
Pre witt 1979
],
where a graph-theoretic model and syntactic pattern recognition are introduced for tissues using

3.2 Feature Extraction in Digital Histopathology 41
two random spatial structures, namely , Dirichlet tessellation and Delaunay tessellation as the
unique topological planar dual graph of the Dirichlet tessellation. These structures are used to
obtain a unique, in vertible, relational and attrib uted graph representation of histological sections.
The work also suggests that Dirichlet’ s domain can be e xtended to a more general Johnsons-Mehl
domain. Another early application of the technique is found in [
Sanfeliu 1981
] for analyzing
muscle tissue patterns using a distance measure between graphs. The syntactic structure analysis
approach though theoretically po werful, has limited usage in practical applications of the present
times due to its e xtensi v e computational requirements, which was also e xperimentally observ ed
by scientists later . Subsequently , with de velopment of digital pathology techniques, some more
ef ficient graph-based methods were analyzed.
The commonly used graph-theoretic methods in histopathology are V oronoi diagrams, De-
launay triangulations and related graphs, cell graphs and attrib uted relational graphs. It has been
stated in [
Gurcan 2009
] that a total of approximately 150 spatial-relational features ha ve been
e xtracted from all graph structures for histological images.
The most ab undantly applied graph-theoretic techniques for representation and analysis
of histological images till date are the V oronoi diagram, Delaunay graph and related graphs.
V oronoi diagrams and Dirichlet domains ha ve been e xplored in [
Darro 1993
] for gro wth as-
sessment of de grees of dif ferentiation in terms of cell population dynamics in Feulgen stained
colorectal neoplastic cell colonies gro wing on histological slides, by studying the structure of
clones in 11 dif ferent media. The authors in [
K eenan 2000
] describe a graph-based approach
for analysis of H&E stained images of Cervical Intraepithelial Neoplasia, where Delaunay
triangulation mesh is first computed on a region and 18 quantitati ve features are e xtracted.
Discriminant analysis is applied to cate gorize image regions into normal or one of the three
cancer grades. Minimum spanning tree has been used ab undantly for quantitati v e representation
of tissue architecture. F or instance, H&E stained tissue sections of colorectal adenomas are
analyzed in [
Meijer 1992
] using MST features to dif ferentiate between three grades of dysplasia.
In [
v anDiest 1992
], the authors analyze in v asi v e breast cancer tissue images using 10 syntactic
structure features e xtracted from corresponding MST of each H&E stained breast tissue re gion. A
method for detecting lymphoc ytic infiltration in HER2 positi v e breast cancer images is described
in [
Basa v anhally 2010
] using features deri v ed from V oronoi diagrams, Delaunay triangulations
and minimum spanning trees.
Cell graphs ha v e been introduced and extensi vely studied in [
Gunduz 2004
], [
Demir 2004
],
[
Demir 2005a
], [
Demir 2005b
] and [
Gunduz-Demir 2007
] for analysis of H&E stained brain
cancer (glioma) biopsy samples. In [
Bilgin 2007
], the authors use hierarchical cell graphs to
model H&E stained breast tissue images. H&E stained histopathological images of bone tissue
samples are analyzed using adv anced v ersion of cell graphs called ECM-awar e cell graphs
in [
Bilgin 2010
]. In the cell graph methods, the authors hav e used lo w magnification (80-100
times) tissue images to construct representati v e graphs, global graph metrics ( e.g . a v erage de gree,
clustering coef ficient, a v erage eccentricity , eigen v alues of graph) are extracted, and the images
are classified with the help of neural networks or SVM classification methods. The cell graph

42 CHAPTER 3. Related W ork in Digital Histopathology
representation is a fle xible method for histological images due to adv antages o ver other geometric
graph-based techniques, especially the use of fe wer geometrical constraints for cancer tissue
modeling. Howe ver , it has limitations when highly magnified histological images need to be
analyzed, and are addressed later in the thesis.
Attrib uted relational graphs (ARG) are emer ging as an appealing topic of interest for re-
searchers in this field. Attrib uted minimum spanning trees are explored for representing Feulgen
stained soft tissue tumors (malignant fibrous histioc ytoma, fibrosarcoma, rhabdomyosarcoma,
osteosarcoma and Askin tumor) in [
Kayser 1991
], where a basic graph is first constructed ac-
cording to the neighborhood condition of O’Callaghan [
O’Callaghan 1975
]. Nuclear features
related to morphometry and DN A-content are attributed to the v ertices, and the diff erences
between features of connected v ertices are attrib uted to the corresponding edges. Resulting
MST is decomposed into clusters using a suitable decomposition function on the edges, and
clusters of distinct nuclear orientation are detected. Then a cluster tree is constructed by defining
the geometric center of a cluster as a ne w v ertex and by computing the neighborhood of the
cluster v ertices. Another study employing re gional adjacenc y graphs using the ARG principle
for content-based retrie v al of H&E stained breast tumor tissue images is giv en in [
Sharma 2012
].
The authors ha v e represented segmented tissue images using ARG, where nodes are centroids
of tissue re gions ha ving attrib utes as a label from the segmentation and morphological features
of the associated se gment. Edges connect neighboring segments and are characterized by edge
attrib utes which are Euclidean distance and perimenter of the common boundary between con-
necting nodes. A* based graph matching algorithm is employed to retrie ve the image re gions
most similar to a gi v en query region. In the study [
Arslan 2013
], attrib ute relational graphs
are applied for model-based segmentation of cell nuclei in flourescence microscop y images of
hepatocellular carcinoma, where primiti ves are defined and the ARG is constructed on these
on the basis of a set of rules. In [
Sharma 2016
], a part of this work has been described, with
introduction of the cell nuclei attrib uted relational graphs for automatic classification of H&E
stained gastric cancer re gions into malignancy le vels based on HER2 immunohistochemistry .
In [
Sharma 2017a
], the work is e xtended using a comparati v e study of de v eloped v ariations in
the proposed graph-based method.
Other less popular graph-based methods include O’ Callaghan neighborhood graphs
[
Kayser 1986
], [
Kayser 1988
], cell cluster graphs [
Ali 2013
] and cell webs [
Ficsor 2008
]. Hy-
brid methods combining architectural information with lo w-le v el features ha v e also been in-
troduced in literature quite early such as [
Kayser 1989
] and [
Kayser 1990
]. A related work
is [
W eyn 1999
], where Feulgen stained tissue sections of malignant mesothelioma, hyperplastic
mesothelium and pulmonary adenocarcinoma are analyzed where a total of 82 features including
te xture features, morphometric features, densitometry features and graph-based features based
on V oronoi diagram, Gabriel graph and MST are extracted from histological sections. Another
recent study is found in [
Doyle 2008
], where H&E stained breast biopsy images are studied.
A total of 3400 textural and architectural features are e xtracted. T exture features include gray
le v el statistical features, Haralick features and Gabor features, and architectural features include

3.3 Machine Learning in Digital Histopathology 43
V oronoi diagram, Delaunay graph and MST -based features. In [
Chekkoury 2012
], H&E stained
breast biopsy images are classified into cancerous and non-cancerous using morphometric,
F ourier -based, Hessian-based, V arma-Zisserman textons and graph-based features.
Summarizing the e v olution of graphs for image description in digital histopathology , the
earliest methods range from the mathematically comple x but less ef ficient tessellations like
Dirichlet and Johnson Mehl tessellations, follo wed by studies on proximity graphs, especially
Delaunay graphs and their subgraphs. Subsequently , graphs based on alternati ve neighborhood
conditions like O’Callaghan and zone of influence were e xplored, promising an edge ov er the
V oronoi-based graphs. Meanwhile, there was also an increased interest in syntactic pattern recog-
nition methods deri ving quantitati ve information from graph representations, mostly utilizing
minimum spanning trees. Ho we v er , with further de v elopments in virtual microscopy techniques,
a paradigm shift has occurred from the constrained (or geometric) methods utilizing proximity
graphs to more relax ed (or flexible) application-specific graphs, for e xample cell graphs and
attrib uted relational graphs applied on dif ferent types of tissues. Man y authors hav e also used a
combination of the lo w-le vel methods and high-le v el graph-based methods for analysis tasks.
It can be concluded that graph-based techniques comprise an important direction in the field
of histopathogical image analysis, and can pro vide the basis for de v eloping v arious automatic
applications and tools to retrie v e and classify tissue sections in a reliable way .
3.3 Machine Lear ning in Digital Histopathology
In this section, prominent literature in digital histopathology that incorporate image analysis
using the four selected machine learning methods, namely , support vector machines, AdaBoost
ensemble learning, random forests and deep con v olutional neural networks are reported.
3.3.1 Support V ector Machines
Support v ector machines ha ve been e xplored e xtensi v ely for supervised learning tasks in his-
tological image analysis. Dif ferent SVM kernels such as linear , Gaussian and polynomial
ha v e been compared for optimizing SVM classification performance of hyper -spectral colon
tissue [
Rajpoot 2004
]. Diagnosis of prostate cancer and Gleason grading in H&E stained histo-
logical images has been attempted using object-based features and supervised learning methods
including SVM [
T abesh 2007
]. Another work is described in [
Altunbay 2010
] using Delaunay
graph representation follo wed by SVM to classify H&E stained colon tissue images into normal,
lo w-grade and high-grade cancer . A pre-analysis component of this research in volv es the auto-
matic detection of necrotic areas in H&E stained gastric cancer images, performed using te xtural
features with SVM learning using discriminati v e thresholds is e xplained in [ Sharma 2015c ].

44 CHAPTER 3. Related W ork in Digital Histopathology
3.3.2 AdaBoost Ensemble Lear ning
AdaBoost ensemble learning is e xplored in [
Doyle 2006
] for classifying H&E prostate adenocar -
cinoma images by e xtracting texture and intensity features and training based on AdaBoost and
decision tree classifiers. In [
Y ang 2009
], breast cancer tissue microarray samples are classified
using te xture features, along with comparison of classical AdaBoost with its v ariations such as
gentle AdaBoost, LogitBoost and other classifiers like Bayesian classifiers and SVM. Prostate
cancer is also analyzed in a multiresolution approach by describing te xture features of tissue
images with AdaBoost ensemble learning in [
Doyle 2012
]. Automatic detection and grading
in prostate cancer histopathological images is performed in [
Gorelick 2013
] using two stages
of AdaBoost classification algorithm. Related to this work, cell nuclei classification in H&E
stained gastric cancer images is studied using morphological characteristics and AdaBoost
classifiers in [
Sharma 2015b
]. Additionally , gastric carcinoma images are classified on the basis
of immunohistochemical response using a range of lo w-le v el and graph-based features along
with their combinations follo wed by AdaBoost ensemble learning in [ Sharma 2016 ].
3.3.3 Random F or ests
Handcrafted image features can be used in conjunction with random forests in a supervised
learning approach for v arious classification and detection tasks in digital histopathology . An
e xample includes [
DiFranco 2011
], where co-occurrence color texture features and random
forest feature selection and ensemble learning are applied for classification of prostate carcinoma
in histological sections from radical prostatectomy . In [
Sommer 2012
], hierarchical learning
workflo w is proposed using random forest classifiers for automated mitosis detection in breast
cancer images. Neutrophils are identified in H&E stained histological images by first con-
structing V oronoi diagrams follo wed by random forest binary classification in [
W ang 2014
].
In [
Sharma 2017b
], random forests are applied as the traditional machine learning method for
comparati v e e v aluation with deep learning, and in [
Sharma 2017a
], random forests hierarchical
classification is performed for image analysis in H&E stained gastric cancer WSI.
3.3.4 Deep Con v olutional Neural Networks
Deep learning using con v olutional neural networks (CNN) is presently recei ving tremendous
attention in di v erse fields of image analysis [
LeCun 2015
]. Earliest applications of deep con v o-
lutional neural networks include handwritten character recognition [
LeCun 1989
], [
Lecun 1998
]
and general object categorization [
Krizhe vsk y 2012
], that are no w e xpanding in dif ferent fields
of image analysis.
Se v eral recent adv ances ha v e been proposed by scientists in the field of digital histopathology
relating to deep learning methods. The introduction of deep learning in this area was enabled
initially by application-specific challenges where lar ge-scale labeled datasets ha v e been made
a v ailable publicly , for instance, the mitosis detection challenge in breast cancer [
V eta 2015
] and

3.4 Image Analysis in Gastric Cancer 45
gland se gmentation challenge in colon histology images [
Sirinukunwattana 2017
]. In literature,
a fe w e xamples of widely kno wn studies of digital pathology using deep con v olutional neural
networks are as follo ws. The work [
Cire ¸ san 2013
] sho ws highly f a v orable result for mitosis
detection in breast cancer images using deep neural networks. A CNN method is proposed
in [
Cruz-Roa 2014
] for classification of in v asi v e ductal carcinoma in breast cancer WSI with
results superior to handcrafted features. U-nets hav e recently become v ery popular for biomedical
image se gmentation [
Ronneber ger 2015
]. These are fully con v olutional neural networks, com-
posed of a con v olution block follo wed by a decon v olution block to generate segmented images
as the output. The approach was initially used for se gmenting neuronal structures in electron mi-
croscopy stacks, and w as further applied to transmitted light microscopy images to perform cell
tracking, depicting most superior results in the ISBI cell tracking challenge [
ISBI 2016
]. A patch-
based con v olutional neural network approach is e xamined in [
Hou 2015
], for discriminating
glioma and non-small-cell lung carcinoma into respecti v e subtypes in histological WSI. A com-
parati v e study of deep CNN architectures with handcrafted methods for classification of stromal
and epithelial histological images of breast cancer and colorectal cancer is gi v en in [
Xu 2016
].
The study [
Sirinukunwattana 2016
] describes a spatially constrained con volutional neural net-
work for cell nuclei detection and classification in colon cancer images. Fully con v olutional
networks ha ve been de v eloped in [
Chen 2016a
], and this method won the MICCAI gland se gmen-
tation challenge [
Sirinukunwattana 2017
]. More research in this area includes [
Spanhol 2016
]
for classification in breast cancer histopathological images, and [
Litjens 2016
] for prostate cancer
classification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes.
A part of this research has been presented in [
Sharma 2017b
] to contrib ute to this field by
analysis of gastric cancer WSI, where a no vel CNN architecture is proposed, quantitati vely
compared with handcrafted features follo wed by traditional machine learning and also with the
Ale xNet deep con volutional frame work.
3.4 Image Analysis in Gastric Cancer
Medical image analysis of gastric cancer has not been e xtensi v ely e xplored in pre vious literature
of digital histopathology . Recent studies comprising of related work illustrate application-specific
techniques for automatically analyzing the gastric tissue. F or instance, gastric atrophy is quanti-
tati v ely analyzed in H&E stained sections using syntactic structure methods in [
Zaitoun 1998
].
Image re gions depicting normal mucosa, gastritis and adenocarcinoma in H&E stained histologi-
cal sections are distinguished by use of cytometric measurements in [
Ficsor 2006
]. The ability
of image processing methods to allo w dif ferentiation between normal and cancerous tissues in
gastric cancer using confocal endomicroscop y has been strengthened in [
Kakeji 2006
]. A semi-
supervised approach for detection and diagnosis of gastric cancer is described in [
Cosatto 2013
]
using multiple instance learning in H&E stained tissue images. A multiresolution approach
introduced in this work to impro v e cell nuclei se gmentation of gastric cancer is e xplained
in [
Sharma 2015b
]. The author has also e xplored necrosis detection in gastric carcinoma using

46 CHAPTER 3. Related W ork in Digital Histopathology
te xtural features and SVM-based classification in [
Sharma 2015c
]. The work in [
Sharma 2016
]
in v olves graph-based analysis of H&E stained g astric carcinoma WSI based on their HER2 im-
munohistochemistry , with a comparati v e study with v ariations in [
Sharma 2017a
]. Additionally ,
the problem of cancer classification and necrosis detection are further addressed using deep
con v olutional neural networks in [ Sharma 2017b ].
3.5 Summary
This chapter mentions the related work in past and recent literature re garding medical image
analysis in digital histopathology , especially , in the domains of feature e xtraction and image
description methods, machine learning approaches and histopathological image analysis of
gastric cancer .

C H A P T E R 4
Ov er view of Resear ch Methodology
Contents
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.2 Stage 1: Pr eparation of Materials . . . . . . . . . . . . . . . . . . . . . . 47
4.3 Stage 2: Image Pre-analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4 Stage 3: Analysis of Cancer Regions . . . . . . . . . . . . . . . . . . . . . 49
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.1 Intr oduction
In this chapter , an ov ervie w of the w orkflo w of the proposed research methodology and conducted
e xperiments is presented, which allo ws the readers to attain a clear understanding of the follo wing
chapters. The schematic ov ervie w of the experimental pipeline is demonstrated in Figure 4.1 . It
consists of three main stages (or phases) named as preparation of materials, image pre-analysis
and analysis of cancer re gions. These are briefly described in the next sections and e xtensi vely
discussed in the follo wing chapters.
4.2 Stage 1: Pr eparation of Materials
In the first stage, histopathological whole slide image data is acquired and processed for sub-
sequent image analysis tasks. It begins with the precise procedures for acquisition of tissue
specimens from rele v ant sources, follo wed by the scanning process to generate digitized whole
slide images. The next step includes image data labeling by e xpert pathologists for dif ferent
modules of the frame w ork, in order to create the gold standar d or gr ound truth datasets. This
stage also comprises intermediate steps such as visual quality assessment and whole slide image
re gistration required to generate consistent image databases necessary for the succeeding analysis
e xperiments.
47

48 CHAPTER 4. Ov ervie w of Research Methodology
Fig. 4.1 Schematic o vervie w of the e xperimental pipeline

4.3 Stage 2: Image Pre-analysis 49
4.3 Stage 2: Image Pr e-analysis
As the name suggests, this stage incorporates certain image processing and analysis tasks which
are a prerequisite before proceeding with the primary research objecti v es of this study . The
necessity of these tasks arises out of thorough examination of the acquired whole slide image
data, and the e v aluation of classical image processing steps follo wed by refinements according
to the particular nature of the histopathological images of gastric cancer .
A fe w challenges are observ ed during this stage, which are required to be addressed by
suitable methods. These are as follo ws.
1.
Necrosis was identified by e xpert pathologists in a small part of the acquired whole slide
image data. So, these necrotic areas are required to be detected and e xcluded from the final
working datasets before continuing with the analysis of cancer .
2.
The working magnification is needed to be finalized in order to achie ve optimal results for
cell nuclei se gmentation and subsequent image analysis tasks. This requires an elaborate
multiresolution e v aluation and may potentially lead to combination of information.
These challenges ha v e led to the de v elopment of two main directions in the pre-analysis stage in
the e xperimental pipeline, namely:
1. Necr osis detection using traditional machine learning and deep learning methods.
2.
Multir esolution enhancement of image se gmentation, which also includes de v eloping a
computerized cell nuclei classification procedure.
Outcomes of this stage are the procurement of histological image datasets after excluding necrotic
areas, determination of the working magnification for further image analysis tasks, procedure
for optimal isolation and cate gorization of cell nuclei, and the proposal of two applications in
digital histopathology , namely , automatic necrosis detection and automatic determination of
tissue composition in whole slide images of gastric cancer .
4.4 Stage 3: Analysis of Cancer Regions
In this stage, analysis of cancer regions is performed in H&E stained gastric cancer whole
slide images based on the malignanc y groups defined by the corresponding HER2 immuno-
histochemical response. This is accomplished using a wide range of methods including no vel
analysis approaches, comparati v e e v aluation with e xisting methods as well as modifications in
the classical methods.
It starts with the generation of working datasets for re gion-based analysis of gastric cancer .
This is follo wed by the design and implementation of handcrafted features and performance
comparison with the state-of-the-art lo w-le v el and high-le v el features in the traditional analysis
route. Additionally , deep learning is explored by design and empirical analysis of CNNs and the
proposal of a suitable CNN architecture. The traditional machine learning and deep learning

50 CHAPTER 4. Ov ervie w of Research Methodology
methods are quantitati v ely and comparati v ely assessed using a comprehensi ve performance
e v aluation scheme for the described cancer classification problem. At the end, visual and
statistical analysis of the image data and extracted features for the tw o staining methods is
performed to conclude the e xperimental findings.
Outcomes of this stage are the accomplishment of research objecti v es for image analysis in
H&E stained gastric cancer whole slide images in digital histopathology , and contribution to tw o
additional applications, namely , computer -aided diagnosis consisting of cancer classification
based on immunohistochemistry , and content-based image retrie v al of regions of interest from
WSI data.
4.5 Summary
In this chapter , the author has introduced and briefly described the three stages of the research
frame w ork, namely , preparation of materials, image pre-analysis and analysis of cancer regions.

C H A P T E R 5
Stage 1: Pr eparation of Materials
Contents
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.2 Whole Slide Image Acquisition . . . . . . . . . . . . . . . . . . . . . . . . 52
5.2.1 Specimen Properties . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5.2.2 Scanner Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5.3 Annotations: HER2 whole slide images . . . . . . . . . . . . . . . . . . . 54
5.3.1 Labeling Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.3.2 Semi-automatic WSI Registration . . . . . . . . . . . . . . . . . . . 55
5.3.3 Annotation T ransformation . . . . . . . . . . . . . . . . . . . . . . . 57
5.4 Initial W orking Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.4.1 Annotations for Cell Nuclei Se gmentation Ev aluation . . . . . . . . . 60
5.4.2 Annotations for Cell Nuclei Classification . . . . . . . . . . . . . . . 60
5.5 Annotations for Necr osis Detection . . . . . . . . . . . . . . . . . . . . . . 64
5.5.1 Datasets for SVM-based Method . . . . . . . . . . . . . . . . . . . . 66
5.5.2 Datasets for Deep Learning Methods . . . . . . . . . . . . . . . . . 66
5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.1 Intr oduction
This chapter describes in detail the procedures follo wed to prepare the materials required
for e xperiments in histopathological image analysis. It includes the process of whole slide
image acquisition, e xpert labeling for generation of ground truth including transfer of labeled
information between the two histological stains, and organization of w orking datasets for
subsequent image analysis tasks comprising this study .
51

52 CHAPTER 5. Stage 1: Preparation of Materials
5.2 Whole Slide Image Acquisition
5.2.1 Specimen Pr operties
Initially , twelve HER2 immunohistochemically stained and corresponding H&E stained sur -
gical specimens are selected from clinical and oncological studies on 454 cases of gastric
carcinoma [
W arneke 2011
], [
W arneke 2013
]. In the related pre vious studies, information from
patients with gastric cancer w as retrie v ed from archi v es of the Institute of Pathology , Uni versity
Hospital Kiel, Kiel, Germany and their study cohort comprised of 454 all Caucasian patients
under gone either total or partial gastrectomy for adenocarcinoma of the stomach or esophago-
gastric junction between 1997 and 2009. The retrie v ed paraf fin blocks were used to obtain 454
whole tissue sections. These sections are stained using the HER2 immunohistochemical method.
The prior selection of twelve whole slide sections follo wed by digitization was originally
performed for the study described in [
Behrens 2015
], and the digitized WSI data along with the
corresponding H&E glass slides were shared for this research. Hence, the selection process to
retrie v e the specified tissue sections was performed remotely in the Department of P athology ,
Christian-Albrechts-Uni v ersity , Kiel, German y by a group of medical experts and the author did
not participate or ef fect the choice of the selected twelv e tissue specimens.
T o summarize, twelve tissue specimens ha ve been acquired from proximal or distal parts of
the stomach. These specimens belong to twelv e patients with one specimen per patient. T issue
specimen acquisition is follo wed by specimen preparation procedure similar to the process
described in Section 2.2.1.2 , and glass slides are obtained using two staining methods, namely ,
HER2 immunohistochemical staining and haematoxylin and eosin staining. The glass slides
were first prepared using HER2 immunohistochemical staining and non-consecuti v e sections
from the same tissue block were later stained with H&E stain. The approximate thickness of the
tissue sections is 4
µm
. The underlying cancer grades based on HER2 immunohistochemical
response of the tissue sections are identified by e xpert pathologists as 0, 1+, 2+, 3+, based upon
immunostaining grades analogous to the rules described by Rüschof f et al [ Rüschof f 2010 ].
5.2.2 Scanner Details
T o digitize the acquired glass slides, two whole slide image scanners are employed. Initially ,
HER2 and H&E stained glass slides were scanned using a Leica SCN400 microscopic whole-
slide scanner at its maximum, nominally 400 times magnification with pixel resolution 0.26
µm
per pix el at 40
×
objecti v e magnification with quadratic pixels. Images were exported
from the scanner system into files of Leica SCN format, which is a multi-image, pyramidal,
multiresolution 64-bit TIFF format. This scanning process was conducted in Christian-Albrechts-
Uni v ersity , Kiel, Germany and resulting digital whole slide images were shared electronically
for the research.
Ho we ver , on careful visual observ ation of the acquired H&E WSI by the author , pathologists
and field e xperts along with the initial e v aluation of cell nuclei image se gmentation results, a fe w

5.2 Whole Slide Image Acquisition 53
issues were disco vered as follo ws. Firstly , when a tissue section is scanned using a single focal
plane, the WSI scanner focuses on one two-dimensional layer in the re gion of interest, ho we v er ,
as the tissue section is three-dimensional, all the observed cells are not simultaneously focused
and result in unfocused neighboring cells, unclear boundaries and reduced sharpness in the digital
whole slide image. The problem is enhanced by non-uniform illumination during the image
acquisition. Due to these ef fects, the automatic se gre gation of cells is a tedious process leading to
unsatisfactory se gmentation results, as observ ed e xperimentally . Therefore, a re-scanning of the
original H&E glass slides was recommended and later implemented using a more sophisticated
scanning technique, as described belo w .
T o ov ercome the challenges in the original H&E WSI data, ele v en H&E glass slides were
transported from Christian-Albrechts-Uni v ersity , Kiel, Germany to Charité Uni versity Hospital,
Berlin, Germany . One corresponding H&E glass slide could not be physically acquired. The
glass slides were re-scanned using 3DHistech P anor amic-250 whole-slide scanner , which has
a higher resolution of 0.22
µ
m per pix el at 40
×
objecti v e magnification with quadratic pix els.
Additionally , it provides an e xtended depth of field [
Bradle y 2005
] using an adv anced digital
image processing technique called F ocus Stacking which combines multiple images tak en at
dif ferent focus distances. Hence, the resulting single in-focus whole slide image has a higher
visual quality compared to the whole slide image generated using a single focal plane. The
a v erage size of each histopathological whole slide image is 13.65 Gigapix els. So, these WSI are
high resolution with high sharpness and good visual quality . Further , the H&E WSI database can
be called heter og eneous with v ariations in malignanc y le v els, stain intensities and inter -patient
biological characteristics.
(a) (b)
Fig. 5.1 Examples of corr esponding sections in (a) HER2 and (b) H&E stains.
In conclusion, ele v en H&E WSI acquired with the help of the second whole-slide scanner
are used for further e xperiments. HER2 stained WSI image data is used later for statistical
comparison in Chapter 9 , otherwise only the pathologists’ marked annotations of HER2 stained
WSI are utilized for generating the ground truth and creating working databases in H&E stained
WSI after re gistration and annotation transformation procedures. An example of a WSI pair in
HER2 and H&E stain used in these studies is demonstrated in Figure 5.1 .

54 CHAPTER 5. Stage 1: Preparation of Materials
5.3 Annotations: HER2 whole slide images
5.3.1 Labeling Pr ocess
HER2 e xpression and gene amplification in the described HER2 whole slide sections is analyzed
in [
Behrens 2015
]. During this procedure, each HER2 immunohistochemically stained WSI is
marked by ten e xpert pathologists with polygon annotations follo wing a 10% cut-off rule based
on HER2 immunohistochemical response of the tissue. The pathologists ha ve remotely mark ed
annotations with the help of a virtual microscopy softw are [
Behrens 2015
]. A screenshot of the
program is depicted in Figure 5.2 . It mainly consists of a vie wer application to display whole
slide images with options for user interaction and assistance tools. The pathologists’ annotations
generated in this process are pro vided along with each HER2 WSI for this research.
Fig. 5.2 Scr eenshot of virtual micr oscopy pr o gram used by patholo gists to cr eate annotations in HER2
WSI based on immunohistochemical r esponse
The HER2 positi v e tumor areas are marked according to the 10% cut-of f rule [
Behrens 2015
],
which consist of gastric cancer grades 2+ and 3+ and are mostly visually distinguishable from
the remaining tissue due to distinct bro wn colored staining. Additionally , HER2 negati ve areas
are also marked, which consist of gastric cancer grades 0 and 1+ and don’ t sho w a distinct
stain response, ho we v er , these are morphologically identified as tumor areas by the patholo-
gists. It can be noted that HER2 positiv e areas define a higher de gree of malignanc y , whereas

5.3 Annotations: HER2 whole slide images 55
HER2 ne gati v e areas denote a lo wer malignanc y le v el. Inter - and intra-observer v ariability and
pathologists’ beha vior for observing the HER2 stained WSI dataset has been discussed in detail
in [
Behrens 2015
]. The a verage number of pix els per annotation for HER2 positi ve tumor are
231.7
× 10 6
and for HER2 ne gati v e tumor are 1279.0
× 10 6
. The distrib ution of number of HER2
positi v e and HER2 neg ati v e annotations for each expert pathologist and each slide is sho wn in
T able 5.1 .
The HER2 WSI are con verted from the scanner format into V irtual Slide F ormat (VSF)
which is a compressed image format consisting of a folder with se v eral files to support a faster
access on single whole slide image. The corresponding annotations are con verted to VMSM
files which are XML-formatted metafiles containing metadata about the slides including the
pathologists’ annotations. These data formats are suitable for accessing whole slide image data
and related annotations with the help of VMscope software support [
VMscope 2010b
]. In order
to create the ground truth using H&E stained WSI, a semi-automatic registration and annotation
transformation procedure is first applied to transform these polygon annotations from each HER2
WSI to the corresponding H&E WSI. This procedure is described in the follo wing sections.
5.3.2 Semi-automatic WSI Registration
Image re gistration is a widely used digital image processing method of aligning two or more
images of the same scene. This process in v olves designating one image as the reference (also
called the reference image or the fixed image), and applying geometric transformations to
the other image(s) so that the y align with the reference [
Zito v a 2003
]. In this work, whole
slide image re gistration is required to transform annotations of pathologists from HER2 WSI
into corresponding annotations in H&E WSI for further e xperiments, as H&E is the working
stain for reasons e xplained earlier . Spatial dif ferences in position and orientation between
corresponding whole tissue sections in two stains are not ne gligible, hence, WSI pairs are first
semi-automatically re gistered to generate spatial correspondence data between pairs of HER2
and H&E whole slide images.
The V irtual Slide Processing En vironment ( V iSPEe : details in Appendix A.4 ) was applied
for creating spatial correspondences between pairs of whole slide images of the same tissue
in dif ferent stains. The program helps the user to manually generate a set of control points
in the WSI pair , allo wing visual selection of common features in each image to map to the
same pix el location. It also creates Delaunay triangulation using the selected control points as
seeds, which is utilized during annotation transformation procedure. The triangulation edges
are called smart links between control points in the same WSI, and this feature also aids the
user to visualize the relati v e positions of control points in order to create good distrib ution
indicated by equal sized Delaunay triangulations to co ver the entire WSI. The output of the
process is an XML file containing correspondences in the form of pixel positions and Dealunay
triangulation information, for each pair of whole slide images. Screenshot of the V iSPEe program
for semi-automatic re gistration in an example WSI pair along with the selected control points
and Delaunay links is sho wn in Figure 5.3 .

56 CHAPTER 5. Stage 1: Preparation of Materials
T able 5.1 Distribution of number of patholo gists’ annotations marked in HER2 WSI on the basis of immunohistoc hemical r esponse (P: pathologist, S: slide, H+:
number of HER2+ tumor annotations, H-: number of HER2- tumor annotations)
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 Annotations
per S
H+ H- H+ H- H+ H- H+ H- H+ H- H+ H- H+ H- H+ H- H+ H- H+ H- H+ H-
S1 565912153451 0 37453424 32 56
S2 6 10 5 10 4 7 2 4 6 10 17 22 6 10 4 8 15 18 10 14 75 113
S3 3657231 3 1 4 341 5 2 4 37352541 0 53 85
S4 14122512122334121 6 1 8 12 29 44
S5 34341223123412244534 23 34
S6 24341223121213121235 16 29
S7 35574523235756564545 39 52
S8 34382323121 0 1 1 34231234 30 44
S9 23252345233745232361 0 29 47
S10 14231223341216123412 16 32
S11 31 5 121223125645121211 6 20 55
Annotations
per P 32 65 35 61 21 36 33 48 24 38 67 98 34 59 26 42 52 68 38 76 362 591

5.3 Annotations: HER2 whole slide images 57
Fig. 5.3 Scr eenshot of V iSPEe pr ogram for semi-automatic r e gistration in one HER2 and H&E WSI pair
5.3.3 Annotation T ransf ormation
Using the stored WSI re gistration information, source coordinates of polygon annotations of
pathologists in HER2 WSI are transformed into destination coordinates in corresponding H&E
WSI. The annotation transformation method requires a mapping
f : R 2 → R 2
for bi v ariate two-
v alued data. It is achie ved using tw o transformation algorithms depending on the position of each
polygon v ertex in the reference polygon annotation of HER2 WSI with respect to the Delaunay
triangulation created during the semi-automatic registration. A local af fine transformation is
applied for all the polygon points inside the con ve x hull of the control point cloud, and global
rigid transformation is applied for all points lying on or outside the con ve x hull of control points,
as e xplained belo w .
5.3.3.1 Interpolation using Local Affine T ransf ormation
If the source point lies inside one of the Delaunay triangles, it is mapped to its destination point
using an af fine transformation function [
Amidror 2002
]. Suppose the source point
P
for which
we want to estimate the mapping is situated at
x = ( x, y )
, inside the Delaunay triangle with
v ertices
P 1
at
x 1 = ( x 1 , y 1 )
,
P 2
at
x 2 = ( x 2 , y 2 )
and
P 3
at
x 3 = ( x 3 , y 3 )
of the reference dataset.
The points and associated v ectors can be represented in af fine coordinate system as sho wn in
Figure 5.4a . From the figure, it can be observ ed that,
x = x 1 + x ′
= x 1 + a 2 ( x 2 − x 1 ) + a 3 ( x 3 − x 1 ) (5.1)
where
a 2 , a 3
are the coordinates of the point
x
in the ne w coordinate system such that
0 ≤
( a 2 , a 3 ) ≤ 1 . Expanding equation 5.1 , we get,

58 CHAPTER 5. Stage 1: Preparation of Materials
(a) (b)
Fig. 5.4 Mapping by linear triangular interpolation of (a) triangle
P 1 P 2 P 3
in the
x, y
plane to (b) triangle
Q 1 Q 2 Q 3 in the u, v plane.


x
y 
 = 

x 1
y 1 
 + 

x 2 − x 1
y 2 − y 1 
 a 2 + 

x 3 − x 1
y 3 − y 1 
 a 3
= 

x 1
y 1 
 + 

x 2 − x 1 x 3 − x 1
y 2 − y 1 y 3 − y 1 
 

a 2
a 3 

(5.2)
Using the abo ve equation, ( a 2 , a 3 ) can be obtained as


a 2
a 3 
 = 

x 2 − x 1 x 3 − x 1
y 2 − y 1 y 3 − y 1 

− 1 

x − x 1
y − y 1 
 (5.3)
Let the mapping of the three Delaunay v ertices in the destination dataset be represented by
u 1 = ( u 1 , v 1 )
,
u 2 = ( u 2 , v 2 )
and
u 3 = ( u 3 , v 3 )
in the
u, v
plane for the source points
x 1
,
x 2
and
x 3
respecti v ely . Using linear triangulation interpolation, the mapping of point
x = ( x, y )
will be a point
u = ( u, v )
inside the destination Delaunay triangle, that has the same relati v e
coordinates
a 2 , a 3
with respect to the ne w coordinate system. This ne w point and co-ordinate
system is sho wn in Figure 5.4b . Using analogy with equations 5.1 and 5.2 , we get,
u = u 1 + a 2 ( u 2 − u 1 ) + a 3 ( u 3 − u 1 ) (5.4)
On e xpanding equation 5.4 and substituting from equation 5.3 , the final value of
( u, v )
is obtained
as,


u
v 
 = 

u 1
v 1 
 + 

u 2 − u 1 u 3 − u 1
v 2 − v 1 v 3 − v 1 
 

x 2 − x 1 x 3 − x 1
y 2 − y 1 y 3 − y 1 

− 1 

x − x 1
y − y 1 
 (5.5)

5.4 Initial W orking Datasets 59
The computation is repeated for all source polygon points lying inside the Delaunay triangulations
generated during semi-automatic re gistration, in order to obtain the transformed destination
polygon points.
5.3.3.2 Interpolation using Global Rigid T ransf ormation
When a source point is not situated inside an y of the Delaunay triangles in the reference dataset,
it is mapped globally with the help of rigid transformations using Singular V alue Decomposition
(SVD) method [
Sorkine 2009
]. A very fe w polygon points are lying outside the boundary of the
tissue section and are situated out of the con ve x hull of the control point cloud (or outside the
Delaunay triangles). These points are interpolated using the global rigid transformation method,
ho we ver , it is scarcely used compared to the local af fine transformation method.
Example of a WSI pair with original and resulting polygon annotations after semi-automatic
re gistration and annotation transformation procedure are sho wn in Figure 5.5 . Red polygons
represent marked HER2 positi ve areas, and blue polygons represent marked HER2 ne gati ve areas.
Fig. 5.5 Example of HER2 and H&E WSI pair containing original and r esulting pathologists’ annotations
after semi-automatic WSI r e gistr ation and annotation tr ansformation pr ocedur e
5.4 Initial W orking Datasets
Initially , smaller working datasets are generated using a fraction of the a vailable H&E WSI data
to perform image pre-analysis e xperiments in the second stage of the research frame work, namely ,
cell nuclei se gmentation and e v aluation, cell nuclei classification, multiresolution enhancement
and determination of tissue composition (partly in necrosis detection). The reason to generate
initial working datasets is to lo wer the time requirements for creating the ground truth consisting
of manual annotations by e xpert pathologists, and also because the in volv ed tasks mainly require
labeling of cell nuclei as data instances, which can be obtained in suffi cient quantities using
fe wer lar ge-sized WSI. Hence, fi v e H&E stained WSI specimens are considered from the entire
a v ailable WSI data for this purpose. Regions of interest are first selected for generating image

60 CHAPTER 5. Stage 1: Preparation of Materials
tessellations using the polygon annotations of pathologists, and consist of three main types of
re gions based on malignancy , namely , HER2 positi ve tumor , HER2 neg ati v e tumor and non-tumor .
Then, these re gions of interest are tessellated at dif ferent objecti v e magnifications ranging from
10
×
to 40
×
to generate non-overlapping image tiles, with the tiles at highest magnification of
size 1024
×
1024 pix els. As pathologists’ annotations are not completely o v erlapping, the selected
tissue areas are enclosed in maximum annotations, ensuring agreement of most pathologists to
minimize inter -observ er v ariability .
From the generated image tessellations, fi ve image tiles are selected from each type of
malignanc y region for each WSI. A total of 5(number of tiles per re gion type)
×
3(number
of re gion types)
×
5(number of WSI) = 75 image tiles comprise the initial working dataset,
where each malignanc y type is represented by 25 tiles. This step results in e v enly distrib uted
initial working datasets with one-third (33.33%) representation of each type of malignancy .
The tiles are selected such that the y contain v ariation in stain intensity and malignanc y le vels,
introducing heterogeneity in the resulting image datasets. Using these initial working datasets,
two main labeling tasks are performed i.e. creating annotations for cell nuclei se gmentation
e v aluation and cell nuclei classification, which are later used for multiresolution enhancement
and determining tissue composition. Additionally , a part of these datasets are included for the
labeling of necrotic re gions (as necrosis was initially disco v ered by expert pathologist in this
dataset). CognitionMaster [
W ienert 2013
], an object-oriented analysis framew ork (details in
Appendix A.4 ), is applied for user interaction with the tissue images for the two cell nuclei
labeling tasks.
5.4.1 Annotations f or Cell Nuclei Segmentation Ev aluation
F or quantitati v ely e v aluating the results of cell nuclei se gmentation algorithm, non-o verlapping
tiles containing unique cell nuclei are required, which are generated in the initial w orking
datasets. Haematoxylin-stained cell nuclei in the 75 image tiles are manually labeled using point
annotations, first on an indi vidual basis and then presented to an e xpert pathologist for v alidation.
The labeling is performed using R OIManag er , a utility plugin in the CognitionMaster program.
The point annotations are carefully created by marking the centroid of each cell nucleus. Each
image tile is annotated at highest resolution i.e . size 1024
×
1024 pix els. The resulting annotation
information of each image tile is sa v ed as a text file, analyzed later during quantitati ve e v aluation
of cell nuclei se gmentation and enhancement. A screenshot of the plugin used for labeling
process, with yello w ‘+’ marks as the point annotations, is sho wn in Figure 5.6 . A total of 25,257
manual point annotations are obtained for the 75 image tiles in the initial working datasets. Their
distrib ution among the three types of re gions is sho wn in Figure 5.7 .
5.4.2 Annotations f or Cell Nuclei Classification
Non-o verlapping image tiles containing unique cell nuclei are also required to obtain reference
data for cell nuclei classification, hence, initial working datasets are used for creating the ground

5.4 Initial W orking Datasets 61
Fig. 5.6 Scr eenshot of R OIMana ger pr ogr am with cell nuclei annotations (yellow ‘+’ marks) made in an
imag e tile at the highest r esolution
Fig. 5.7 Distrib ution of point annotations among the thr ee types of r e gions (number and per centa ge of
total annotations)
truth data for this purpose. The inputs for this step are cell nuclei contours obtained the result of
automatic se gmentation of the tissue images. This step in v olv es the manual labeling of pre viously
se gmented cell nuclei contours with one of the se ven cell nuclei types or classes. The se ven
cell nuclei classes ha v e been suggested by expert pathologists and are named as epithelial cell ,
leukocyte , fibr ocyte (or bor der cell) , conglomer ate , fr agment , other cell (including blood cell in
vessel) and artefact . Figure 5.8 illustrates the se v en defined cell nuclei classes.

62 CHAPTER 5. Stage 1: Preparation of Materials
(a) (b) (c) (d) (e) (f) (g)
Fig. 5.8 Defined cell nuclei classes (a) Epithelial cell (b) Leukocyte (c) F ibr ocyte (d) Conglomerate (e)
F r agment (f) Other cell (including blood cell in vessel) (g) Artefact.
The defined cell nuclei classes are e xplained as follo ws.
Epithelial cells
are either elliptical
or round shaped, typically larger than leuk oc ytes with a distinct texture and characteristic of
tumor -af fected areas.
Leukocytes
are deeply stained round cells.
F ibrocytes
are long shaped
cells, which are generally found as border cells of v essels, and may also include smooth muscle
cells and fragments of these types of cells.
Conglomerates
consist of se gments containing more
than one cell and badly se gmented cells such as a parts of multiple cells.
F ragments
, as the
name suggests, are the se gments which are only parts of a cell nucleus.
Other cells
include the
cells in the tissue which are not stained by haematoxylin, e .g. blood cells inside a v essel, hence,
not marked by point annotations.
Artefacts
are se gments i.e. not a cell nucleus b ut may appear
to be like one, also not mark ed by point annotations.
Nearly one-half of the se gmented image data from the initial working datasets is manually
labeled for creating ground truth for cell nuclei classification. This includes a total of 33 image
tiles from the fi v e H&E WSI at 40
×
objecti v e magnification. The same image tiles are also
labeled at 30
×
objecti v e magnification (768
×
768 pix els), as this magnification shows potential of
good se gmentation results which can be utilized in multiresolution combination for segmentation
enhancement (details in Section 6.3 ). This has resulted in manual generation of cell nuclei
annotations in a total of 66 image tiles.
Cell nuclei se gments are labeled first by the author , which are later revie wed by an expert
pathologist. A plugin called Object-Manag er is used in conjunction with CognitionMaster
for the manual labeling of cell nuclei se gments. This program can be operated by the user in
two modes, namely , ‘classify mode’ and ‘re vie w mode’. In the classify mode, each contour
is labeled for the first time and the resulting annotations are sa v ed along with sample contour
information in an XML file, of the same name as the image tile, called learning sample file
(LSF). In the re vie w mode, classes of pre viously labeled contours can be modified, and the
corresponding LSF is updated. A screenshot of the plugin in re vie w mode, consisting of already
labeled contours (dif ferent colors depicting dif ferent cell nuclei classes) in an image tile at 40
×
objecti v e magnification is sho wn in Figure 5.9 . A total of 17,516 cell nuclei segments in 30
×
and 25,848 cell nuclei se gments in 40
×
ha v e been annotated as one of the se ven cate gories
using the described method. The distrib ution of the manual annotations in v arious classes of the
cell nuclei se gments in the three type of regions, and total annotations in each type of re gion
is demonstrated in Figure 5.10 (a) for 30
×
and Figure 5.10 (b) for 40
×
objecti v e magnification,
respecti v ely .

5.4 Initial W orking Datasets 63
Fig. 5.9 Scr eenshot of the Object-Manager pr ogr am in r evie w mode showing labeled contours in an
imag e tile at highest r esolution
(a)
(b)
Fig. 5.10 Distrib ution of cell nuclei annotations for seven classes in eac h of the thr ee types of r e gions
and total annotations at (a) 30 × objective magnification (b) 40 × objective ma gnification.

64 CHAPTER 5. Stage 1: Preparation of Materials
5.5 Annotations f or Necrosis Detection
One e xpert pathologist has observed and mark ed necrotic areas to create the ground truth for
necrosis detection in two distinct w ays, namely , at a low magnification and at the highest
magnification. This labeled information is then used to generate three datasets for the initial
e xperiments consisting of non-ov erlapping image tiles of dif ferent sizes. The three initial working
datasets for necrosis detection are e xplained as follo ws.
1. Annotations in WSI - splitting appr oach:
T o generate the first dataset, rectangular regions
of interest (R OI) are selected in two whole slide images according to three groups, namely ,
HER2 positi v e tumor , HER2 negati ve tumor and non-tumor , depending on the associated
HER2 polygon annotations. All the WSI with rectangular R OIs are presented to an e xpert
pathologist, who has marked polygon annotations of necrotic areas at the WSI le v el after
visual assessment in predefined re gions of interest. On visual inspection, it is found that the
marked areas are lar ger and more prominent in tw o WSI for the gi v en rectangular R OIs. These
lar ger necrotic areas and surrounding non-necrotic areas are split into non-ov erlapping image
tiles of specific sizes at the highest magnification. Hence, this dataset has been generated using
a splitting appr oac h , and includes well-defined images of the two types. A verage number of
pix els per polygon annotation labeling necrosis in the WSI are 48.01
× 10 6
. The rectangular
re gions of interest and polygon necrotic annotations are marked using the VMscope program
VM Slide Explor er [
VMscope 2010b
]. A screenshot of the VM Slide Explor er program and an
e xample WSI sho wing rectangular re gions of interest in the three types of malignanc y groups
(HER2 positi v e: yello w , HER2 ne gati v e: blue and non-tumor: green), and necrotic polygons
marked inside them (red) at lo w magnification (0.65 × ) is depicted in Figure 5.11 (a).
2. Annotations in image tiles - merging appr oach:
The second dataset contains image tiles
from four whole slide images, and are a subset of the initial working dataset of size 1024
×
1024
belonging to the three malignanc y groups. The pathologist has labeled the image tiles at the
highest magnification i.e . at 40
×
, with each square annotation is made at the smallest size of
64
×
64 pix els. Labels are mapped from smaller to lar ger tiles using the information at the
smallest size. Class of a lar ger sized image tile assigned as the one with maximum instances
of constituent smaller image tiles. Hence, this dataset is created using a mer ging appr oac h
and possesses a higher visual comple xity . Object-Manager plugin of the Co gnitionmaster
software (e xplained in Section 5.4.2 ) is applied to create annotations for this purpose. A
screenshot of the Object-Manag er plugin and an e xample of image tile from a non-tumor
re gion with square annotations created at the smallest tile size is sho wn in Figure 5.11 (b),
where necrotic tiles are marked in blue squares and non-necrotic in green squares.
3. Combined dataset:
The third dataset contains the combination of first and second datasets by
blending the ground truths created by e xpert pathologist using two dif ferent approaches. It is
considered comprehensi v e as it contains heterogeneity in terms of visual distinction of image
tiles with respect to stain intensity , malignancy le vels and biological v ariations, required in
order to de v elop a potentially rob ust necrosis detection system. Image tile sizes and number
of image tiles of each size in the respecti v e datasets are sho wn in T able 5.2 .

5.5 Annotations for Necrosis Detection 65
(a)
(b)
Fig. 5.11 Scr eenshots of (a) VM Slide Explor er pr ogr am and an e xample WSI showing r ectangular
r e gions of inter est in the thr ee types of malignancy gr oups (HER2 positive: yellow , HER2 ne gative: blue
and non-tumor: gr een), and necr otic polygons marked inside them (r ed) at 0.65
×
objective magnification
(b) Object-Manag er plugin and an e xample of image tile fr om non-tumor r e gion with squar e annotations
cr eated at the smallest tile size, wher e necr otic tiles ar e marked with blue squar es and non-necr otic in
gr een squar es at 40 × objective magnification.
T able 5.2 Number of image tiles of dif fer ent sizes in the thr ee datasets for necr osis detection using
SVM-based method
Image tile size Using splitting
appr oach
Using merging
appr oach
In combined
dataset
64 × 64 7680 3790 11470
128 × 128 1920 1039 2959
256 × 256 480 291 771
512 × 512 120 78 198
1024 × 1024 30 18 48

66 CHAPTER 5. Stage 1: Preparation of Materials
5.5.1 Datasets f or SVM-based Method
The combined dataset of image size of 512
×
512 pix els is used as the final working database
for necrosis detection using traditional machine learning with SVM, with a total of 198 non-
o verlapping image tiles. It must be specified that the selection of this size is due to the follo wing
reasons. Firstly , when the specified classification methods (described in Section 6.2 ) are applied
on the combined dataset specified in T able 5.2 , most fa v orable results are achie ved using image
sizes 512
×
512 and 64
×
64. These observ ations ha v e been discussed in detail in [
Sharma 2015c
].
The R OC curves obtained for the combined dataset is sho wn in Figure 5.12 (a), clearly suggesting
that the choice of the two sizes for image tessellations. As a larger field of vie w is always
significant and preferable for analysis of histological images, the lar ger size of 512
×
512 pix els
is considered. Another reason is the uniformity of image size for all methods, including deep
learning. The distrib ution of the image tiles among the three types of malignancy re gions is
sho wn in Figure 5.12 (b). It can be observed that the image tiles are generated mostly from the
HER2 ne gati v e tumor and non-tumor malignancy re gions, as smaller necrotic areas ha v e been
recognized by e xpert pathologist in the HER2 positi v e tumor regions.
5.5.2 Datasets f or Deep Lear ning Methods
The final working database for necrosis detection using deep learning methods also consists
of image tiles of the most con venient and performance optimized pix el size i.e . 512
×
512
pix els. Further , the size is consistent with the deep learning method applied for the analysis
of cancer re gions. The dataset has been obtained using data augmentation methods on the
e xisting non-ov erlapping images, (detail in Section 7.2 ). Using this process, 47,128 image tiles
are generated and their distrib ution among the three types of malignancy re gions is sho wn in
in Figure 5.13 . Data augmentation has been performed using the initial image tiles of size
1024
×
1024 to generate nearly uniform datasets with images belonging to HER2 ne gati v e tumor
and non-tumor malignanc y regions, due to smaller necrotic areas in the HER2 positi v e tumor
re gions not suf ficient to create image tiles at the gi v en pix el size.
5.6 Summary
This chapter pro vides a detailed description of the first stage of the research frame work, i.e. ,
preparation of materials. In this stage, WSI data has been acquired from rele v ant sources, labeled
with the help of domain kno wledge of medical e xperts, and processed and or ganized into work-
able databases for subsequent image analysis e xperiments. It begins with the WSI acquisition
from gastric cancer tissue specimens, follo wed by expert labeling of polygon annotations based
on the immunohistochemical response in HER2 stained WSI, semi-automatic re gistration and
annotation transformation to the H&E stained WSI, and generation of the initial w orking datasets.
Ground truth is obtained as reference data in the form of point and contour annotations for
cell nuclei se gmentation and classification procedures respecti v ely of the image pre-analysis
stage. This stage also includes the process of labeling necrosis in the gastric cancer tissue for the
necrosis detection task.

5.6 Summary 67
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
False positive rate
True positive rate

64x64
128x128
256x256
512x512
1024x1024

(a)
(b)
Fig. 5.12 (a) R OC char acteristics for combined datasets using the described SVM-based method showing
comparative performance between dif fer ent imag e tile sizes (b) Distrib ution of labeled image tiles for
necr otic and non-necr otic tissue in thr ee types of malignancy r e gions and total image tiles in the final
dataset for SVM-based method
Fig. 5.13 Distrib ution of labeled imag e tiles for necr otic and non-necr otic tissue in HER2 ne gative tumor
and non-tumor types of malignancy r e gions and total ima ge tiles in the final dataset for deep learning
methods

C H A P T E R 6
Stage 2: Image Pr e-analysis
Contents
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6.2 Necrosis Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
6.2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
6.2.2 Moti v ation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.2.3 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
6.2.4 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
6.3 Cell Nuclei Segmentation and Evaluation . . . . . . . . . . . . . . . . . . 79
6.3.1 Se gmentation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 79
6.3.2 Se gmentation Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 82
6.4 Multiresolution Segmentation Enhancement . . . . . . . . . . . . . . . . 84
6.4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
6.4.2 Cell Nuclei Classification . . . . . . . . . . . . . . . . . . . . . . . 85
6.4.3 Multiresolution Combination . . . . . . . . . . . . . . . . . . . . . . 93
6.5 A pplications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.5.1 Automatic Necrosis Detection . . . . . . . . . . . . . . . . . . . . . 95
6.5.2 Automatic Determination of Histological T issue Composition . . . . 96
6.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
6.1 Intr oduction
In general, image preprocessing includes methods for image quality improv ement, for e xample,
image contrast enhancement and brightness correction, such that the resulting images are more
suitable for subsequent image processing and analysis. The gastric cancer WSI specimens ha ve
been acquired in good quality using highly adv anced digitization equipment. Consequently ,
69

70 CHAPTER 6. Stage 2: Image Pre-analysis
impro vement of image quality is not considered essential and is outside the scope of this
study . Ho we v er , certain tasks can provide additional information from the images aiding further
analysis, for instance, image re gistration and segmentation, and can be grouped into the image
pre-analysis stage. The image pre-analysis stage in the described process pipeline is highly
specialized to w ards digital histopathology , and includes task-specific methods such as necrosis
detection, cell nuclei se gmentation and multiresolution segmentation enhancement of the tissue
images of gastric cancer WSI datasets. Among these, image re gistration has been explained in
detail in Section 5.3 due to its high significance in the preparation of materials.
Relating to the necrosis detection methods described in this chapter , a preliminary fea-
sibility study using te xtural features and SVM machine learning to establish the choice of
appropriate patch sizes and demonstrate a suitable classification performance was published
in [
Sharma 2015c
]. A comparati v e examination of deep learning methods and traditional
random forests machine learning for this classification problem form the partial contents
of [
Sharma 2017b
]. Further , the multiresolution segmentation enhancement aproach w as intro-
duced in [
Sharma 2015b
] using AdaBoost ensemble learning, b ut the methodology described in
this work is an e xtension of the initial study with required modifications (details in Section 6.4.2 ).
6.2 Necr osis Detection
As necrosis was visually detected in small parts of g astric cancer WSI, the first pre-analysis step
is to e xclude the necrotic tissue regions using a computer -based method. This step is comparable
to noise r emoval in general image pre-analysis, ho we ver , the ‘noise’ here refers to entire necrotic
areas in the WSI which are undesirable for further histopathological image analysis procedures.
Hence, necrosis can be considered analogous to, b ut dif ferent from image noise which consists
of unwanted signals adding spurious information on the original image.
6.2.1 Backgr ound
Necrosis or necrotic cell death, is premature cell death which may occur due to se v eral factors
such as infection, heat, mechanical injury or chemicals. According to the Nomenclature Com-
mittee on Cell Death (NCCD), necrosis is lar gely defined in a neg ati v e fashion as cell death
which lacks apoptotic or autophagic features [
Kroemer 2009
]. It is morphologically charac-
terized by cellular changes, e.g . gain in cell v olume, swelling of cell organelles, rupture of
plasma membrane, moderate chromatin condensation, follo wed by disappearance of intracellular
contents [
Kroemer 2009
]. In [
Majno 1995
], necrosis is defined as drastic and visible changes
which appear in the tissue after cell death, which can be “hinted at by or dinary histological
tec hniques” . The process of necrosis in a cell is illustrated in Figure 6.1 .
Necrosis occurring in tissues af flicted with malignancies is kno wn as tumor necr osis . T umor
necrosis is a usual feature of solid tumors caused from chronic ischemic injury o wing to rapid
tumor gro wth [
Swinson 2002
], and can be classified as focal, moderate or e xtensi v e, depending

6.2 Necrosis Detection 71
Fig. 6.1 Schematic r epr esentation of the pr ocess of necr osis in a cell
on its occupanc y in the tumor area [
Pollheimer 2010
]. F ocal necrosis has a central appearance as
the tumor is depleted of oxygen supply , a condition called tumor hypoxia, in the central portion
of tumor . There are usually li ving cancer cells in neighborhood that can be further observed by
pathologists or computer -based analysis tools for diagnostic purposes and treatment planning.
In literature, necrosis has been usually studied at the cytological le vel by considering the cel-
lular morphology and biochemical processes. F or instance, a re vie w about necrosis and apoptosis
detection and discrimination, presenting a selection of techniques using electron and fluorescence
microscopy and dif ferent cell markers is presented in [
Krysko 2008
]. At the histological le v el, tu-
mor necrosis has been found to be an important prognostic factor . The prognostic v alue of tumor
necrosis in histological specimens has been quantitati v ely analyzed with respect to biological v ari-
ables in [
Leek 1999
], [
Muro-Cacho 2000
], [
Swinson 2002
], [
Sengupta 2005
], [
Langner 2006
],
and [
Pollheimer 2010
] for breast carcinoma, gastrointestinal stromal cancer , lung cancer , renal
carcinoma, upper urinary tract carcinoma, and colorectal cancer respecti v ely . These studies
indicate that the properties of tumor necrosis can be correlated with the type and e xtent of the
respecti v e tumors, and sho w significant impact on tumor prognosis.
A related work for classifying histological image signatures of Glioblastoma images into
necrosis, apoptosis and viable re gions has been performed in [
Le 2012
], b ut their method uses a
small set of training images (50 patches of size 80
×
80 pix els), and performs classification by
reconstruction from subspace analysis, which the authors state is “usually slow” and takes man y
hours to learn small images, hence, not suitable for datasets containing a lar ge number of images.
In this work, the author has implemented a f ast and accurate computer -based method performing
dedicated task of necrosis detection using image analysis techniques for heterogeneous whole
slide images of gastric cancer , which can be potentially generalized for dif ferent types of tissues.
6.2.2 Motiv ation
Automatic detection of necrosis in histological images is an interesting problem of digital
histopathology that needs to be addressed. It may be more dif ficult to reach diagnostic conclusions
by observing a WSI containing necrotic re gions, and manual identification of necrosis using
visual inspection can be a time-consuming task for lar ge-sized whole slide images. Automatic
necrosis detection can pro vide useful information about the the type and extent of malignanc y and
help in formation of prognosis, as higher necrosis may promote tumor gro wth and consequently

72 CHAPTER 6. Stage 2: Image Pre-analysis
lead to a lo wer possibility of survi v al [
V akkila 2004
]. In some cancer patients, (neoadjuv ant)
chemotherapy is follo wed by surgery and histological diagnosis, where determining the e xtent
of necrosis can pro ve useful. Moreo ver , the detected necrotic areas can be e xcluded in order
to carefully analyze the remaining li ving tissue. Therefore, necrosis detection can constitute a
preparatory stage that is helpful to pathologists and subsequent analysis tools for more precise
disease observ ation and characterization.
The prime moti v ation behind the step in the described experimental pipeline is, while
observing gastric cancer tissue whole slide images, fe w necrotic re gions ha ve been identified
visually by e xpert pathologist. Detection and subsequent exclusion of necrotic re gions has been
recognized as a necessary task for computer -based analysis to succeed, otherwise necrosis will
distort the percei v ed information and will interfere in the decision-making process. Hence, these
re gions are required to be excluded from the WSI datasets before performing more sophisticated
cancer analysis tasks on the remaining li ving tissue. So, automatic necrosis detection method
has been de v eloped as a pre-analysis step which solv es the problem in a timely manner .
6.2.3 F eatur e Extraction
When observ ed in histological slides, tumor necrosis has a patch-like appearance with contracted
or disappeared cell nuclei in the extracellular space, so it can be visually distinguished from
remaining li ving tissue on the basis of these typical and discernible te xtural characteristics.
Thus, for this study , it is hypothesized that automatic pattern recognition methods using texture
information can be suitably applied for detecting necrotic areas in histopathological images
of gastric cancer . Lo w-le vel state-of-the-art te xtural image features are extracted from image
patches to ef ficiently represent the visual characteristics of necrotic and non-necrotic areas in the
tissue, primarily because these areas can be dif ferentiated on the basis of their te xtural appearance
without the kno wledge of the underlying tissue architecture.
A combination of gray le v el co-occurrence matrix features and Gabor filter -bank texture
features is e xtracted from the histological image tiles comprising the necrosis detection datasets,
each at 40
×
objecti v e magnification and size 512
×
512 pix els. This texture-based description
is used in order to achie v e an optimum balance of algorithm ef ficienc y and simplicity , as
necrosis detection is a pre-analysis step which requires to be performed using a quick and precise
implementation. The computed te xtural characteristics are defined as follo ws.
1. Gray le vel co-occurr ence matrix featur es:
The gray le v el co-occurrence matrix descriptors
include the 14 statistical features e xplained in detail in Section 2.3.1.1 . These features belong
to the state-of-the-art te xture features used frequently in digital histopathology .
2. Gabor filter -bank features:
Gabor filter -banks are selected to detect necrosis for simulating
the human visual detection abilities, due to reasons discussed in Section 2.3.1.1 . Image tiles
are filtered using the real parts of Gabor filter kernels of 16 dif ferent frequency , orientation and
standard de viation combinations. The first and second order statistics of the filter responses

6.2 Necrosis Detection 73
are deri v ed, based on least squared error for simplicity , and a total number of 32 Gabor
filter -bank features is used to represent image te xture.
An e xample of a pair of necrotic and non-necrotic image tile and a subset of the extracted te x-
tural features using GLCM statistics and Gabor filter -banks are pictorially depicted in Figure 6.2 .
The pictorial representations sho w apparent dif ferences in the texture characteristics of the tw o
cate gories, ensuring that the extracted features are potentially po werful texture discriminators
that can be utilized in combination with traditional machine learning for ef ficient automatic
necrosis detection in gastric cancer histopathological whole slide images.
(a) (b)
Fig. 6.2 Imag e tile pair and te xtur e featur e pictorial r epr esentation using (a) GLCM statistics (b) Gabor
filter kernels.
6.2.4 Machine Lear ning
After the e xtraction of a set of 46 textural features from each image tile, traditional machine
learning is performed using a supervised learning method, namely , support vector machines
follo wed by discriminati ve thresholding. Later , deep learning methods are also explored for the
problem, i.e . , AlexNet CNN frame work, proposed CNN architecture and ensemble of the two
CNNs. The details of machine learning algorithm are described as follo ws. Using the training
datasets and the method with optimal performance, necrotic areas are detected in regions of
interest in the whole slide images and e xcluded prior to the generation of final working datasets
for analysis of cancer re gions in the third stage of research frame work.

74 CHAPTER 6. Stage 2: Image Pre-analysis
6.2.4.1 Support V ector Machines with Discriminative Thr esholds
Support v ector machines (SVM) is a group of non-probabilistic supervised learning methods that
were first introduced by Boser , Guyon and V apnik in [
Boser 1992
]. Like an y machine learning
method, the goal of SVM is to generate a model from the training data that predicts the target
v alues of test data gi ven its features or attrib utes. Support vector machines aim at finding an
optimal separating hyperplane using the training data to classify the incoming instances.
The classical SVM method is originally defined for binary classification. Initially , gi v en
a training set
( x 1 , y 1 ) , ( x 2 , y 2 ) ... ( x m , y m )
of
m
pre viously seen attrib ute-label pairs such that
attrib utes
x ∈ R n
and class labels
y ∈ {± 1 }
, where
{ x ∈ X , y ∈ Y }
, the mapping
X → Y
needs to be learned using a classifier
y = f ( x, α )
, where
α
represents the parameters of the
classifier function. The easiest approach is the use of linear classifiers, with the decision function
gi v en by ,
f ( x ) = w · x + b (6.1)
where
w
represents the weights and
b
is the of fset (or bias). Howe ver , for complex datasets
consisting of a nonlinear feature space, linear classifiers may not be optimal. Hence, the
method is ele v ated to use of nonlinear classifiers which produce maximum margin hyperplanes
[
Boser 1992
], [
Cortes 1995
]. This is performed by first pre-analysis the data by mapping it to a
richer , nonlinear feature space i.e. x → φ ( x ) and the decision function no w becomes
f ( x ) = w · φ ( x ) + b (6.2)
In the nonlinear case, the SVM requires the solution to the follo wing quadratic optimization
problem [ Boser 1992 ], [ Cortes 1995 ],
Minimize: 1
2 || w || 2 + C
m
X
i =1
ξ i
subject to:
y i ( w · φ ( x i ) + b ) ≥ 1 − ξ i , ξ i ≥ 0 (6.3)
Ho we ver , the dimensionality of
φ ( x )
can be v ery high, thereby making it dif ficult for repre-
sentation in memory and solving it. For this purpose, a
ker nel trick
is applied. The Repr esenter
theor em [
Kimeldorf 1970
] sho ws that (for SVM as a special case),
w
can be represented as a
linear combination,
w =
m
X
i =1
α i φ ( x i ) (6.4)

6.2 Necrosis Detection 75
Hence, instead of optimizing
w
,
α i
can be optimized. After substituting
w
, the decision rule no w
becomes:
f ( x ) = w · φ ( x ) + b (6.5)
=
m
X
i =1
α i φ ( x i ) φ ( x ) + b (6.6)
=
m
X
i =1
α i K ( x i , x ) + b (6.7)
where,
K ( x i , x ) = φ ( x i ) φ ( x )
represents the kernel function. Se veral k ernels ha v e been proposed
by researchers o ver the past fe w years, ho we v er , the most popular ones are, for instance, linear ,
sigmoid, Gaussian and polynomial kernels [ Scholk opf 2001 ].
Gaussian or radial basis function (RBF) [
V ert 2004
] is used as the kernel function for the
necrosis detection task and is defined belo w as,
K ( x i , x ) = e − γ ( || x i − x || 2 ) , γ > 0 (6.8)
where
γ = 1
2 σ 2
and
|| x i − x ||
denotes Euclidean distance between the attrib utes. The choice
of RBF kernels is suitable for the necrosis datasets as the y can non-linearly map samples into
a higher dimensional space. The two e xperimental parameters required to be pre-selected for
SVM training and classification are
c
and
γ
[
Chapelle 2002
], where
c > 0
controls the trade-of f
between mar gin maximization and error minimization and
γ
is the kernel parameter respecti vely .
P arameter selection of optimum parameters
c
and
γ
is performed using a grid search with k-fold
cross v alidation (
k = 5
) [
Chang 2011
], where all
( c, γ )
parameter combinations from a specified
grid of parameter v alues are e xhausti v ely applied, and the one with the best cross v alidation
accurac y is selected. The result of grid search for the giv en training samples is depicted in
Figure 6.3 , where optimal parameters c = 8 and γ = 0 . 5 are applied for further analysis.
Fig. 6.3 P arameter selection using grid sear ch for necr osis detection using SVM

76 CHAPTER 6. Stage 2: Image Pre-analysis
During the classification process, probability estimates are also generated along with each
prediction [
W u 2004
], [
Chang 2011
]. The predicted label is obtained as the one with the lar gest
v alue of probability estimate. A detailed observ ation of the predicted labels and probability
estimates on the training dataset confirms that there are some patches with borderline proba-
bility estimates classified as the opposite class. Hence, thresholds on probability estimates are
introduced as additional e xperimental parameters, kno wn as discriminative thr esholds . These
parameters aim to reduce confusion of the learning method, where a higher confusion to classify
into a gi v en cate gory is associated with lo wer probability estimate v alues. Based on the discrimi-
nati v e thresholds and probability estimate v alue of each instance, final class labels are assigned
according to the follo wing:
L i = 




y ij , if P ( y ij | X ) ≥ T j
1 − y ij , otherwise .
(6.9)
where
L i
is the final class label of instance
i
with original class labels
y ij ∈ { 0 , 1 }
of class
j
gi v en
observ ation
X
.
T j
denotes the threshold on class
j
. Selection of discriminati ve thresholds is
performed using an iterati v e refinement procedure to achie v e optimum classification performance
on the training data using a k-fold cross v alidation (
k = 5
) in the range
[0 . 5 , 1]
at discrete
interv als of 0.05. The threshold search is sho wn in Figure 6.4 as a color scale representation
using blue to red for lo wer to higher v alues of classification error rates. The v alues with least
classification error rate can be observed for
T 1 = 0 . 55
and
T 2 = 0 . 6
. This method provides
fle xibility in classification of doubtful cases and takes into consideration the heterogeneous
nature of the whole slide images.
Fig. 6.4 Selection of discriminative thr esholds for SVM classification
After observing the e xperimental results on final working datasets from Section 8.2 , it can be
seen that using te xtural features and support vector machines with discriminati ve thresholds, a

6.2 Necrosis Detection 77
satisfactory v alue of av erage o v erall and balanced classification accuracy is achie ved as
87.66 %
,
suggesting that the described classes are easily distinguished by the classifiers.
Due to a satisfactory cross v alidation performance using the abov e described features and
machine learning method for necrosis detection in gastric cancer WSI, more comple x traditional
machine learning methods using ensemble learning are not further explored. It should be
emphasized that, as this stage still being the pre-analysis stage, the primary goal is to perform the
required tasks using a fast and accurate algorithm, which has already been achie ved. Howe ver ,
con v olutional neural networks are additionally e xplored due to the nov elty of the research
problem with respect to deep learning methods. Additionally , it is performed to e xamine the
generalizability of the proposed CNN architecture, that was originally designed for the analysis
of cancer re gions b ut can theoretically achie ve accurate classification by directly using the
image kno wledge without an y prior requirement of handcrafted features. Hence, subsequent
e xperiments are performed for necrosis detection using deep con volutional neural networks,
which are described in detail in the follo wing section.
6.2.4.2 Deep Lear ning
Deep learning methods ha v e been explored for detecting necrosis in gastric cancer histopatho-
logical images. A significant interest has been sho wn lately in the image analysis field for deep
con v olutional neural networks, due to their ability to replace the requirement of hand-engineered
image descriptions with direct processing, representation and learning from raw images. This
property has been e xploited for necrosis detection without any prior e xtraction of handcrafted
features. The widely kno wn Ale xNet CNN [
Krizhe vsk y 2012
] and a self-designed CNN archi-
tecture ha v e been trained from scratch and deployed to study the performance of con v olutional
neural networks for the necrosis detection problem. An ensemble of the two CNNs is also tested
for this purpose. Due to requirement of lar ge-scale datasets in deep learning, data augmentation
is performed (discussed in Section 7.2.2 ). The proposed CNN architecture applied to necrosis
detection is sho wn in Figure 6.5 . The selection of e xperimental parameters, as well as training
and deployment ha ve been performed using an elaborate process, described later in Section 7.4.2 .
Examples of learning curv es are demonstrated in Figure 6.6 for randomly selected training round
using the Ale xNet CNN frame work and the proposed CNN architecture.
Fig. 6.5 Pr oposed CNN ar chitectur e for necr osis detection

78 CHAPTER 6. Stage 2: Image Pre-analysis
(a) (b)
Fig. 6.6 Examples of learning curves of r andom training r ounds for necr osis detection using (a) Ale xNet
CNN frame work (b) pr oposed CNN ar chitectur e.
As observ ed in the experimental results of Section 8.2 , cross v alidation yields an ov erall
and balanced classification accurac y for AlexNet CNN method as
99.71 %
. For the proposed
CNN architecture, these are calculated as
93.56 %
. An ensemble of the two CNNs provides
impro vement in performance compared to the indi vidual CNNs, with balanced classification
accurac y as
99.89 %
. A high cross validation accurac y with lo wer false positi v es highlights a
successful classification of necrotic and non-necrotic tissue in gastric cancer histopathological
images using deep learning methods.
There is a minor dif ference in detection rates of the e xplored machine learning methods, which
sho ws that necrosis detection is a f airly simple classification problem compared to the cancer
analysis problem considered later , due to a distinct appearance characteristics of necrotic versus
non-necrotic or li ving areas. One dra wback of the described analysis is, due to a limited amount
of training data consisting of visual information from four WSI, a possibility of misclassification
may e xist among the regions of WSI from unkno wn patients, that are not learned by the classifiers.
This can be attrib uted to the heterogeneity among tissue characteristics of indi vidual patients,
o wing to biological v ariance in a population. In the worst case, necrotic regions which need
to be excluded from further e xamination are misclassified as non-necrotic, and the final goal
may not be completely achie v ed. T o pre v ent this ef fect during the creation of the final working
datasets comprising information of the se v en non-labeled WSI for the analysis of cancer regions,
it is atleast safe to e xclude the automatically classified necrotic regions from further analysis.
Additionally , a brief visual inspection of the non-necrotic classified regions follo ws the automatic
necrotic detection in order to ensure that the false ne gati ve necrotic re gions are not included
in the final working datasets for analysis of cancer re gions. The detailed e xperimental results
and observ ations for these methods are discussed in Section 8.2 and associated computational
requirements are summarized in Appendix A.5 .

6.3 Cell Nuclei Se gmentation and Ev aluation 79
6.3 Cell Nuclei Segmentation and Evaluation
After e xclusion of necrotic regions, the ne xt step is to isolate the cell nuclei in the H&E stained
tissue images using appropriate se gmentation methods. Se gmentation is a crucial and challenging
step in most histological image analysis problems, and performance of subsequent tasks like
feature e xtraction and classification lar gely depends on the results of the segmentation algorithm.
It is dif ficult mainly due to the comple x appearance of cells due to unclear cell boundaries
and o verlaps, hence, manual and automatic segre gation of cell nuclei is a tedious process. A
recently de v eloped and adv anced computerized cell nuclei se gmentation algorithm is used for
this purpose, and its se gmentation results are utilized to perform analysis of cancer regions in the
subsequent stages of the research work.
6.3.1 Segmentation Algorithm
The cell nuclei se gmentation algorithm [
W ienert 2012
] e xplored in the study is a minimum-
model method as it requires minimal apriori information to detect contours of cell nuclei in
H&E stained tissue images. It is a fully-automatic approach and has been applied on the gastric
cancer image tiles to discern the constituting cell nuclei. The cell nuclei segmentation algorithm
consisting of six main steps is defined as follo ws.
The algorithm starts with the detection of all possible closed contours reg ardless of their size,
shape or intensity in the grayscale-transformed image using a gradient-based technique. It uses
a con ventional contour tracing approach for binary images [
Hufnagl 1983
], extended for the
use of grayscale images. Each ro w is a one-dimensional function of intensity
I ( x )
and contour
pix els are recognized as those at which gradient between pair of neighbors is maximum and
these pix el positions are stored. Subsequently , 8-connected neighbors of the stored positions
are scanned to find all possible contours. A v alid contour is the one where start and end pix els
are the same. This step results in multiple o verlapping contours, which are e v aluated based on
gradient features of the input image in the second step.
In the ne xt step, contour e v aluation is performed by computing a metric called Contour V alue
to select contours that best represent image objects. It is defined as the combination of two
contour -based features, namely , mean contour gradient that measures the relati ve importance
of objects in a group of o verlapping objects, and gradient fit that determines which of se veral
alternati v e contours best represents a certain object. For this purpose, firstly a Sobel operated
image
S
is computed by con v olving the image
I
with 3
×
3 Sobel filter operators
G x
and
G y
.
This is gi v en as,
| S | = q ( I ∗ G x ) 2 + ( I ∗ G y ) 2 (6.10)

80 CHAPTER 6. Stage 2: Image Pre-analysis
where, Sobel filter operators G x and G y are [ Gonzales 2009 ],
G x = 



− 1 0 1
− 2 0 2
− 1 0 1



 G y = 



− 1 − 2 − 1
0 0 0
1 2 1




(6.11)
From the Sobel image
| S |
, the mean contour gradient
M G i
for the
i th
contour
C i
,where
p ij
represents the j th contour pix el, is calculated by ,
M G i = P j | S ( p ij ) |
| C i | (6.12)
Also, gradient fit
GF i
which represents the relati v e number of contour pix els which ha ve local
maxima of gradient is measured as,
GF i = P j p max
ij
| C i | (6.13)
where,
p max
ij = 




1 , if max {| S ( p ab ) |} = | S ( p ij ) | ∀ ( a, b ) s.t. x j − 1 ≤ a ≤ x j +1 ∧ y j − 1 ≤ b ≤ y j +1
0 , otherwise
(6.14)
Contour v alue C V i will then be computed as
C V i = M G i · GF i (6.15)
In the third step, a non-o verlapping se gmentation is generated where the enclosed areas of
the ranked contours are labeled in a tw o-dimensional map of the same size as the original image
in descending order of contour v alues, and blocking o v erwriting of already assigned pixels.
Subsequently , the segmentation produced is impro v ed using a no vel contour optimization
method in v olving elimination of non-compact pixels which do not belong to the object using
Manhattan metric. A distance
d
is defined for testing the compactness of object pixels, and a
pix el
p i
with
d i < d
is compact if connected to another pixel
p j
with
d j = d
o ver
d − d i
edges.
Pix els connected ov er more than d − d i edges are remo v ed, and this results in compact objects.
An optional cluster separation step in v olv es detection and separation of conca ve objects by
remo ving pixels around a cutting line passing through the conca vity . Finally , cell nuclei are
detected by assessing nucleus-specific Hematoxylin within each contour area. Hematoxylin
strength is found by color decon v olution [
Ruifrok 2001
] and all objects with intensity greater
than a predefined threshold [ Otsu 1979 ] are selected as cell nuclei objects.
A set of optimal parameters for cell nuclei se gmentation in H&E images of gastric cancer is
selected after visual inspection of results for a wide range of in v olv ed algorithm parameters. The
definitions and optimal v alues of parameters are sho wn in T able 6.1 .

6.3 Cell Nuclei Se gmentation and Ev aluation 81
T able 6.1 P ar ameter selection in cell nuclei se gmentation algorithm
Name of parameter Definition Optimum value f or gas-
tric cancer H&E images
minContourLength
Minimum length of acceptable
contours 50
maxContourLength
Maximum length of acceptable
contours 500
distance d
defined for testing compactness
of object pixels 3
mindepthabs
Minimum absolute depth of con-
ca vity during conca ve object sep-
aration
0
mindepthrel
Minimum relati v e depth of con-
ca vity during conca ve object sep-
aration
0.3
recursi v e
Boolean v alue whether the
conca ve object separation step
should be repeated until no
conca ve objects are found
TR UE
A se gmentation map is a data structure that is used to store spatial positions of image objects
after se gmentation. It encapsulates a two-dimensional array of unsigned inte gers that has the size
of the input image, where the unsigned inte gers are the identifiers of the corresponding se gment
of a gi v en pix el. For each image tile, its corresponding se gmentation map is generated and stored.
Cell nuclei se gmentation of gastric cancer image tiles yields a result similar to the one sho wn in
Figure 6.7 . The yellow outlines are the resulting contours of se gments obtained after the cell
nuclei se gmentation is ex ecuted.
(a) (b)
Fig. 6.7 Cell nuclei se gmentation r esult e xample at 25
×
objective magnification (a) Original ima ge (b)
Pr ocessed imag e showing r esulting cell nuclei se gments.

82 CHAPTER 6. Stage 2: Image Pre-analysis
6.3.2 Segmentation Evaluation
The initial working dataset consisting of 75 image tiles is used in order to e valuate the perfor -
mance of the cell nuclei se gmentation algorithm at dif ferent magnifications. The cell nuclei are
first manually located with point annotations at highest resolution (40
×
) to create the ground
truth which is v alidated by an e xpert pathologist (details in Section 5.4.1 ).
It is intuiti v e that highest amount of useful information should be contained in higher
magnifications, b ut it is also important to observe the ratio between the rele v ant and irrele v ant
information and ho w to dif ferentiate between them. So dif ferent resolutions are simultaneously
considered and visual and quantitati v e assessment is performed to v erify this assumption. Each
image tile is first automatically se gmented with the help of the described cell nuclei segmentation
algorithm at the selected magnifications of 10
×
, 15
×
, 20
×
, 25
×
, 30
×
and 40
×
, hence, a total of
450 se gmented image tiles are generated. Follo wing segmentation, the results are quantitati v ely
compared against the ground truth information containing positions of cell nuclei as point
annotations. The total number of point annotations N p are defined as,
N p =
N im 40x
X
i =1
n pi (6.16)
where
n pi
represents the number of point annotations in the
i th
image tile and
N im 40x
is the total
number of image tiles at 40
×
magnification. Also, for a gi v en magnification, the total number of
se gments obtained N s are denoted by ,
N s =
N im x
X
i =1
n si (6.17)
where
n si
represents the number of se gments found in the
i th
image tile and
N im x
is the total
number of image tiles at magnification X. The follo wing quantities are measured and compared
for each of the six magnifications.
1.
The per centage of corr ectly detected cell nuclei can be calculated as the fraction of all the
point annotations which are enclosed inside a cell nuclei se gment, represented as,
P d = 

1
N p
N im x
X
i =1
n pi
X
j =1
f { p j ( x, y ) ∈ S i } 
 × 100 (6.18)
where
p j ( x, y )
denotes the
j th
point annotation in the
i th
image tile containing a set
S i
of
two-dimensional spatial point coordinates lying inside or on all the detected se gments.
f { P }
is a conditional operator which gi v es 1 when
P
is true, and 0 other wise. Hence, a cell nucleus
is considered as detected if its associated point annotation is inside or on an y of the detected
se gments, so it is marked as a cell nucleus in the ground truth data. Since the segments are
non-o verlapping, each cell nucleus will be detected only once at maximum.

6.3 Cell Nuclei Se gmentation and Ev aluation 83
2.
The per centage of se gments not cell nuclei , denoting the amount of o verse gmentation, can
be calculated as the fraction of the total number of segments which do not contain an y point
annotation i.e . ,
P os = 

1
N s
N im x
X
i =1
n si
X
j =1
f { n p ( s j ) = 0 } 
 × 100 (6.19)
where where
n p ( s j )
represents the number of point annotations lying inside or on the
j th
se gment
s j
, and the summation term denotes the total number of se gments not representing
actual cell nuclei in the images. This percentage denotes the se gments representing false
positi v e instances, which mostly constitute smaller fragments of cell nuclei and other tissue
components.
3.
The per centage of conglomer ates is computed by finding all those se gments which contain
more than one point annotation as a fraction of the total number of se gments, and represents
the number of cell nuclei clusters, i.e . , segments containing more than one cell nucleus.
P c = 

1
N s
N im x
X
i =1
n si
X
j =1
f { n p ( s j ) > 1 } 
 × 100 (6.20)
(a) (b) (c) (d) (e) (f)
Fig. 6.8 Example of cell nuclei se gmentation r esults of an ima ge at dif fer ent objective magnifications for
visual inspection (a) 10 × (b) 15 × (c) 20 × (d) 25 × (e) 30 × (f) 40 × .
Fig. 6.9 Cell nuclei se gmentation performance at individual magnifications

84 CHAPTER 6. Stage 2: Image Pre-analysis
Example of cell nuclei se gmentation results of an image at dif ferent objecti v e magnifications
for visual inspection is depicted in Figure 6.8 . The ov erall result sho wing the three performance
quantities at each magnification can be summarized in Figure 6.9 . From the outcome of the
described coarse-to-fine se gmentation analysis using visual and quantitati v e assessment, it is
observ ed that the percentage of correctly detected cell nuclei increases with magnification. Also,
o verse gmentation (se gments not cell nuclei) increases with magnification. No ov erse gmentation
for 10
×
and 15
×
magnifications indicates that the number of annotated cell nuclei are equal
to or greater than the number of se gments, which means that each segment corresponds to one
or more cell nuclei point annotations. The number of conglomerates decrease with increase in
magnification. In order to preserv e the finer details in the tissue images and capture maximum
number of correctly detected cell nuclei, one choice is the highest magnification 40
×
. Ho we ver ,
to deal with the problem of o verse gmentation caused due to nuclear fragmentation e vident mostly
in 40 × , it may be combined with a lo wer magnification to achie ve more accurate results.
F or identifying which of the lo wer magnifications is suitable for combining with 40
×
objecti v e magnification, a pairwise comparison is performed between segmentation results of
40
×
and each of the lo wer magnifications. The goal is to find the percentage of correctly
se gmented nuclei in lo wer magnifications which can additionally contrib ute to the total correctly
se gmented nuclei with 40 × , and is calculated as P ad x by ,
P ad x = N d x − N dc
N p ! × 100 (6.21)
where
N d x
denotes the number of correctly detected cell nuclei found at magnification X and
N dc
is the number of correctly se gmented nuclei common in both magnifications X and 40
×
.
It is found almost equal (
≈
5%) for magnifications between 15
×
to 30
×
. It is lo wer for 10
×
(
≈
4%). The other factor used for deciding the other magnification is clustering which leads to
formation of conglomerates. It is already seen that clustering decreases with magnification, thus,
in order to minimize it, the ne xt magnification with minimum conglomerates (2.8%) i.e . 30
×
is
selected. Hence, after e v aluating the results of nuclei se gmentation algorithm it is concluded
that se gmentation information at 30
×
and 40
×
objecti v e magnifications will be utilized for
se gmentation enhancement.
6.4 Multir esolution Segmentation Enhancement
6.4.1 Backgr ound
As observ ed after the detailed e v aluation of se gmentation results, it is clear that visual information
contained in more than one resolutions needs to be combined to achie v e desired results for further
analysis of cancer re gions. Therefore, an appropriate improv ement in this direction can be the
use of multiresolution methods. In pre vious literature, there ha v e been a fe w studies relating
to multiresolution analysis in digital histopathology . For instance, a multiresolution texture

6.4 Multiresolution Se gmentation Enhancement 85
analysis technique is used in [
Shuttle w orth 2002b
] for classifying colon cancer images that
focuses on v arying distances of te xture co-occurrence matrix instead of spatial resolution, and
does not include isolation of nuclei before classification. A multiresolution approach is also
reported in [
K ong 2009
] for neuroblastoma images where higher magnification is considered
only when the results from lo wer magnifications are unsatisf actory , using a feedback loop
in v olving pathologists. Another work related to cell classification reports a multi-class and
two-stage cate gorization of Wright stained WBCs [
Ramesh 2012
]. In the first stage, cells are
classified as ones with se gmented and non-segmented nucleus, and in the second stage as one
of the fi v e subtypes. In the author’ s paper [
Sharma 2015b
], a part of the work including cell
nuclei se gmentation e v aluation, cell nuclei classification (using AdaBoost ensemble learning)
and resulting multiresolution se gmentation enhancement ha ve been published.
6.4.2 Cell Nuclei Classification
Combining information at the le v el of segmentation itself is a non-tri vial task, due to which
automatic classification of cell nuclei se gments is required to be performed. This work empha-
sizes on object-le v el classification of cell nuclei se gments in gastric cancer images based on their
morphological, texture, and color and intensity characteristics. The se v en cell nuclei classes
ha v e been suggested by expert pathologists and are named as epithelial cell, leuk ocyte, fibroc yte
(or border cell), conglomerate, fragment, other cell (including blood cell in v essel) and artefact
(details in Section 5.4.2 ).
Initial working datasets are used to generate ground truth data and performance e valuation.
As e xplained in Section 5.4.2 , 33 tiles were initially selected at 30
×
and 40
×
objecti v e magnifi-
cations to generate the ground truth annotations, ho we v er , after performing necrosis detection,
labeled se gments from 24 tiles at 40
×
magnification ha v e been retained for the final training
sample as remaining tiles contain total or partial necrotic regions. A balanced dataset has been
created for training purpose containing equal number i.e. 400 samples per cate gory , depending
on the relati v e a v ailability of training samples of each class in the labeled data, hence, a total of
2,800 samples are employed for training the classifiers using the supervised traditional machine
learning methods.
In [
Sharma 2015b
], two dif ferent classification approaches hav e been e xplored. In the first
approach, single-stag e classification of segments is performed into eight predefined classes.
Ho we ver , in later experiments, a modification i.e . combination of clusters and badly se gmented
cells as conglomerates has been introduced. In the second approach, hierar c hical classification
is e xecuted, where, in first stage, segments are classified into three broad classes, namely ,
compact objects (including epithelial nuclei, leukoc ytes, fibrocytes/border cells, other nuclei
and nuclei fragments), conglomerates (including clusters of nuclei and badly se gmented nuclei)
and artefacts, and in the second stage, each class objects are further classified into one of the
respecti v e subclasses. Hierarchical classification is a v oided in the described experiments because
this approach does not result in an y significant improv ement as noted in [
Sharma 2015b
], hence,
unwanted addition to the comple xity of the algorithm is pre v ented. Ho we v er , the explored

86 CHAPTER 6. Stage 2: Image Pre-analysis
traditional machine learning methods are e xpanded from just AdaBoost in [
Sharma 2015b
]
to SVM and random forests for further in vestigations. Another modification is the exclusion
of 30
×
labeled samples in the set of training samples, again after the initial observ ations
in [
Sharma 2015b
], suggesting lo wer classification rates. Hence, for quantitati v e e v aluation and
classification of unkno wn objects, only labeled samples at the highest resolution are used in the
training dataset to reduce time and space requirements of the proposed method.
6.4.2.1 F eatur e Extraction
In this step, object-le v el feature e xtraction is performed where numerical features are computed
on the cell nuclei se gments. A feature vector of 32 cell nuclei features based on morphology ,
te xture, color and intensity are extracted from se gments, as described belo w . A listing of the 32
cell nuclei features with their mathematical descriptions is summarized in T able 6.4.2.1 .
1. Morphological featur es
: Shape or morphological properties are used by pathologists to
identify or distinguish between dif ferent types of nuclei components. The following mor -
phological features are applied, namely , Object Pix els, Minimum Distance to T essellation
Border , Pixels at Layer Border , Maximum Distance to Border , Aspect Ratio of Bounding
Ellipsoid, Minor Axis of Bounding Ellipsoid, Major Axis of Bounding Ellipsoid, Angle
of Bounding Ellipsoid, F orm Factor of Contour , Con v exity of Contour , Length of Contour ,
Area of Contour , Form F actor of Con ve x Hull, Length of Con v e x Hull, Area of Con ve x
Hull [
Hufnagl 1984
], Feret, Minimal Radius of Enclosing Centered Circle, Maximal Radius
of Enclosed Centered Circle, Roundness and Form F actor [
Zerbe 2008
]. For the subset of
feature definitions from [
Hufnagl 1984
], the ax es projections and Freeman code is gi ven in
Figure 6.10 .
(a) (b)
Fig. 6.10 Morphological featur e definitions of a contour (a) Axes pr ojections (b) F r eeman code. Adapted
fr om [ Hufnagl 1984 ].
In addition, the fractal dimension of an object is also used as a morphological feature. It is
a ratio determining the comple xity of a gi v en object or pattern by comparing the changes

6.4 Multiresolution Se gmentation Enhancement 87
in the pattern to the scale of measurement. It was first used for the idea of self-similarity
of objects in [
Mandelbrot 1967
], [
Mandelbrot 1983
]. Fractal dimension has been recognised
as an important tool for cancer analysis [
Baish 2000
]. For e xample, it has been pro ved
in [
Simeono v 2006
], that fractal dimension can play a role in dif ferentiating between tumor
cells in mammary glands in cytological specimens. For a Euclidean space
R n
, fractal dimen-
sion is measured as Minkowski dimension or box-counting dimension [
F alconer 2004
]. It is
calculated by using the box-counting algorithm, where a uniform grid is considered on the
object with each box of side
ϵ
. The number of boxes co v ering the object will be
N ( ϵ )
, as this
number will gro w as
(1 /ϵ ) d
when
ϵ → 0
, i.e . when the box size is reduce by making the grid
finer . Hence,
N ( ϵ ) = lim
ϵ → 0  1
ϵ  d
(6.22)
d = lim
ϵ → 0
l og N ( ϵ )
− l og ϵ (6.23)
The fractal dimension
d
has been estimated in this work in tw o steps, by first counting the
number of filled box es
N ( ϵ )
in a cell nuclei se gment at each corresponding
ϵ
in a predefined
range, and then solving linear re gression using least-squares method [ Intriligator 1978 ].
2. Color and intensity featur es
: Color and intensity features are important for histological
images due to the specific stains used. The features used to characterize the intensity of
se gments in this work include Mean Intensity , Mean Intensity on Contour , Standard De viation
of Intensity , Standard De viation of Intensity on Contour [
Hufnagl 1984
], Contour V alue and
Gradient Fit [
W ienert 2012
]. Mean Chromaticity is also calculated, where chromaticity for
i th
RGB pix el
p i
is defined as the minimum euclidean distance
d min
between pix el RGB
v alue and points on the diagonal where each point
p d
is defined by RGB v alue
R = G =
B = x, x ∈ { 0 , 1 , .. 255 }
. In other words, it is the minimum euclidean distance of a pix el to a
gre y pixel v alue.
d min ( p i , p d ) = min ( ∥ p i − p d ∥ ) p i , p d ∈ I R 3 (6.24)
3. T extur e featur es:
T exture is also a widely used characteristic in histological image analysis,
and v aries with dif ferent tissue components. F our features based on gray le v el co-occurrence
matrix [
Haralick 1973
], namely contrast, entropy , energy and homogeneity ha ve been selected
to represent the visual characteristics of cell nuclei segments. Contrast denotes v ariation in
pix el intensities, entrop y is a statistical measure of randomness, ener gy measures repetiti veness
of te xture patterns and homogeneity quantifies extent of similarity in the image. For high
computational ef ficienc y in the image pre-analysis stage, these features are considered to be a
representati v e subset of the set of GLCM features.

88 CHAPTER 6. Stage 2: Image Pre-analysis
T able 6.2 List of object-level featur es computed for cell nuclei classification
F eatur e name F eatur e definition Remarks
Minimum Distance to T essel-
lation Border Min{ d i from border of image} d i
: distance of
i th
pixel of the object to
image border
Pixels at Layer Border
Number of pixels in the object at the
border of the image
Maximum Distance to Bor -
der Max{ d i from border of image}
Object Pixels | S j | S j
is the set of two-dimensional point coor -
dinates constituting the j th object
Minor Axis of Bounding El-
lipsoid
Length of minor axis of circumscrib-
ing ellipse
Major Axis of Bounding El-
lipsoid
Length of major axis of circumscrib-
ing ellipse
Aspect Ratio of Bounding El-
lipsoid
1000 (Major Axis of Bounding Ellipsoid)
Minor Axis of Bounding Ellipsoid
Angle of Bounding Ellipsoid
Angle between major axis of circum-
scribing ellipse and the horizontal
plane
Length of Contour
Numerical contour length
+( √ 2 −
1) S K P
SKP: Number of contour points with a
ske wed (odd) Freeman direction
Form F actor of Contour 10 (Length of Contour) 2
Geometric area of segment
Con ve xity of Contour 100 · 2( D x + D y + D m + D p )
3 (Numerical contour length) + S K P
Numerical contour length is the number
of points in the contour of the object and
D y , D x , D m , D p
are the total number of
contour points in Freeman directions except
in x , y , x+y and x-y axes respecti vely .
Area of Contour
Number of pixels inside contour (e x-
cluding contour pixels)
Length of Con ve x Hull
Number of pixels at boundary of
con ve x hull
Con ve x hull is the con vex en v elope of
S j
is
the smallest con vex set of points that con-
tains S j .
Form F actor of Con ve x Hull 10 (Length of Con ve x Hull) 2
Geometric area of con ve x hull
Area of Con ve x Hull Number of pixels in con v ex hull
Feret
Max{Euclidean distance between
two points in an object}
Minimal Radius of Enclos-
ing Centered Circle
Radius of smallest circle enclosing
the object
Maximal Radius of Enclosed
Centered Circle
Radius of lar gest circle enclosed by
the object
Roundness 4 (Area of Contour)
π (Feret) 2
Form F actor 4 π (Area of Contour)
(Numerical contour length) 2
Fractal Dimension d = lim ϵ → 0 log N ( ϵ )
− log ϵ
ϵ
is the length of box side,
N ( ϵ )
is the num-
ber of boxes co v ering the object in box-
counting algorithm
Mean Intensity 1
n P n
i =1 I i
I i
is the intensity of the
i th
pixel and
n
is
the number of pixels in the object

6.4 Multiresolution Se gmentation Enhancement 89
Mean Intensity on Contour 1
n c P n c
i =1 I i
I i
is the intensity of the
i th
pixel and
n c
is
the number of pixels in the contour of the
object
StdDe v Intensity q 1
n P n
i =1 ( I i − µ ) 2 µ is Mean Intensity of object
StdDe v Intensity on Contour
q 1
n c P n c
i =1 ( I i − µ c ) 2 µ c is Mean Intensity on Contour of object
Mean Chromaticity
1
n P n
i =1 d i,min
,
d i,min ( p i , p d ) =
min ( ∥ p i − p d ∥ )
Minimum euclidean distance
d i,min
for
i th
RGB pixel
p i
in segment where
p d
is a diagonal RGB point where
p d ( R ) =
p d ( G ) = p d ( B ) .
Gradient Fit P j p max
ij
| C i | Details in Section 6.3.1
Contour V alue M G i · GF i
M G i
is mean gradient of
i th
object, details
in Section 6.3.1
Contrast GLCM P N − 1
i =0 P N − 1
j =0 | i − j | 2 p ( i, j ) p ( i, j )
is the
( i, j )
v alue of GLCM and
N
is the number of gray le vels in the image
Entropy GLCM P N − 1
i =0 P N − 1
j =0 p ( i, j ) l n ( p ( i, j ))
Ener gy GLCM P N − 1
i =0 P N − 1
j =0 [ p ( i, j )] 2
Homogeneity GLCM P N − 1
i =0 P N − 1
j =0
p ( i,j )
1+ | i − j |
6.4.2.2 Machine Lear ning
After the feature e xtraction process, each of the cell nuclei objects are represented by a set of 32
deri v ed object-le v el measures. The cell nuclei balanced dataset consisting of learning samples
is used in a supervised approach. Three machine learning methods, namely , support vector
machines, AdaBoost and random forests are comparati vely analyzed for the classification of
cell nuclei objects into one of the se v en predefined classes. Cross v alidation methods such as
k-fold stratified shuf fled split and lea v e-a-sample-out are applied to quantitati v ely e v aluate the
classification performance of the v arious machine learning methods. The calculated performance
metrics, results and observ ations for each method are discussed in detail in Section 8.3 . Using the
selected learning samples and the machine learning method with optimal performance, unkno wn
cell nuclei objects can be automatically classified into one of the se v en dif ferent classes.
Support V ector Machines
T raining and deployment of SVM is similar to the approach elaborated in Section 6.2.4 us-
ing Gaussian kernels. Ho we v er , in this case, the SVMs are trained for a multi-class classi-
fication problem instead of a binary one, which is implemented using the one-a gainst-one
approach [
Knerr 1990
], [
Chang 2011
]. Discriminati v e thresholds are av oided to pre vent com-
ple xity due to multiple classes. The parameter selection process for SVM, as pre viously discussed,
is used to determine two e xperimental parameters
c
and
γ
for optimum training. Parameter selec-
tion is performed using grid search on the training samples with k-fold cross v alidation (
k = 5
),
and the pictorial result of the grid search is depicted in Figure 6.11 . Optimal parameters
c = 512
and
γ = 0 . 125
are selected and applied for cross v alidation using the entire training dataset.

90 CHAPTER 6. Stage 2: Image Pre-analysis
Fig. 6.11 P arameter selection using grid sear ch for multi-class SVM classification of cell nuclei
After quantitati v e assessment of classification performance, the a v erage ov erall cross v alidation
accurac y achie v ed is
60.39%
and a v erage balanced cross v alidation accurac y is
60.41%
using
support v ector machines as the machine learning method.
AdaBoost Ensemble Lear ning
A multi-class AdaBoost classification algorithm has been de v eloped for cell nuclei classification
task. AdaBoost [
Freund 1995
] or Adapti v e Boosting is the most widely used form of boosting,
and in v olves ensemble learning where a collection of component classifiers or learners is
used and a joint decision is taken by combining their predictions. AdaBoost allows adding a
sequence of weak learners to the algorithm, until a desired lo w training error is achie v ed. Each
weak learner consists of decision stumps. The ensemble of weak learners can be defined as
h k ( x ) , k = 1 , 2 , ..., K max
.
K max
is an e xperimentally determined parameter of the algorithm,
and as e xpected, higher the v alue of
K max
, higher is the time complexity of the algorithm. The
strong learner is assembled from all weak learners through a weighted majority v oting scheme.
The binary AdaBoost algorithm [ Bishop 2006 ] is e xplained as follo ws.
Input: Consider N input v ectors x 1 ... x N comprising the training data.
Output: Binary v ariables y n ∈ {− 1 , +1 } ∀ n ∈ { 1 , 2 , ...N }
Steps:
1. Initialize weights by setting: w (1)
n = 1 / N ∀ n ∈ { 1 , 2 , ...N }
2. For k = 1 ...K max :
(a)
T rain classifier
h k ( x )
on training data by minimizing the weighted error function gi v en
as:
E k =
N
X
n =1
w ( k )
n × 


1 h k ( x n )  = y n
0 otherwise

6.4 Multiresolution Se gmentation Enhancement 91
(b) Ev aluate: ϵ k = E k
P N
n =1 w ( k )
n
(c) Ev aluate: α k = ln [ 1 − ϵ k
ϵ k ]
(d) Update weights as:
w ( k +1)
n = w ( k )
n × 


e α k h k ( x n )  = y n
1 otherwise
3. Final vote of the test point x is gi ven by: H ( x ) = sg n [ P K max
k =1 α k h k ( x )]
The binary classification algorithm is extended to multi-class by considering the final v ote as
the class with majority v otes of weighted binary learners. In general, boosting in volv es the
sequential minimization of an e xponential error function gi v en as [ Bishop 2006 ],
E =
N
X
n =1
w ( k )
n e − 1
2 y n α k h k ( x n ) (6.25)
The parameter
K max
has been e xperimentally determined by using an exhausti ve search
that calculates classification error on randomly selected parts of the training data, for each
v alue of
K max
in a predefined range until no further improv ement is observ ed. Figure 6.12
sho ws the result of the parameter search for
K max ∈ [1 , 50]
. It can be observed that the global
minima of classification error is obtained for
K max = 7
after which classification error increases
non-uniformly with less significant drops at other v alues lik e 10 and 14. T o minimize the
computational requirements and optimize classification accurac y ,
K max = 7
is selected for
AdaBoost ensemble learning. After quantitati v e e v aluation of the multi-class classification
performance, the av erage o verall cross v alidation accuracy achie ved is
63.38%
and a v erage
balanced cross v alidation accurac y is
63.89%
using AdaBoost ensemble learning as the machine
learning method.
Fig. 6.12 P arameter selection for multi-class AdaBoost classification of cell nuclei

92 CHAPTER 6. Stage 2: Image Pre-analysis
Random F or ests
The latest successful traditional machine learning method for supervised classification after
prior computation of suitable handcrafted features is the random forest. A decision tree or
classification tree [
Rokach 2014
] is a predicti v e model that gro ws by using a series of binary
splits called recursi v e splitting (or partitioning), until terminal nodes called leaves are reached,
which are the decision nodes and dominated by one of the classes in the classification process.
Decision trees ha v e se v eral adv antages, such as easy interpretation, ignoring redundant v ariables,
handling of missing data and good performance with lar ge datasets. Se veral e nsemble classifiers
are constructed with more than one decision trees, for instance, random forests, bagging trees,
boosted trees, and rotation forests.
Random forests operate by constructing multiple decision trees during the training phase,
and the classification result is the mode of the classes predicted by constituent decision trees
[
Breiman 2001
]. During the training phase, the random forest gro ws many classification trees,
where each constituent decision tree of the random forest gro ws by selecting a random sample
m
out of
M
input v ariables (or features) at each node and splitting the node using best split on these
v ariables. During the classification phase, the input feature v ector is passed through each of the
decision trees in the random forest. Each decision tree predicts a class, and the class with the
majority v otes is the final result. In this work, instead of majority v oting of indi vidual decision
trees, their probabilistic classification scores
p k
are a v eraged [
Pedre gosa 2011
] to obtain class
probabilities P for an input x and number of trees N t , as belo w .
P ( x ) = 1
N t
N t
X
k =1
p k ( x ) (6.26)
Random forests are superior to decision trees as they pre vent o v erfitting and also enhance the
estimation ability of indi vidual trees. The v ariance among decision trees is also decreased due to
the a v eraging operation.
The random forest algorithm requires a prior selection of two e xperimental parameters,
namely , number of trees
N t
and number of features
m
to consider for best split. Examples of
frequently used v alues of
m
are
√ M
and
l og 2 M
. F or the described classification experiments,
these are estimated internally , during the run using bootstrap a ggr e gation , where each ne w
decision tree is fit from a bootstrap sample of the training observ ations
z i = ( x i , y i )
. An out-
of-bag (OOB) error is computed as the a verage error for each sample
z i
using predictions from
the trees that do not contain
z i
in their respecti v e bootstrap sample [
Friedman 2001
]. The OOB
error is computed for each v alue of
N t
in a predefined range and each number of features i.e.
square root,
l og 2
or all of the total features to approximate a suitable v alue of
N t
at which
the error stabilizes. Figure 6.13 sho ws the result of the parameter search for
N t ∈ [1 , 1500]
,
where minimum and more stable v alues of OOB error are seen for square root of features for
N t ≥ 1000
. In order to reduce the trade-of f between time comple xity and classification accuracy ,
lo west possible number of trees, i.e .
N t = 1000
with features as square root for best split, are

6.4 Multiresolution Se gmentation Enhancement 93
Fig. 6.13 P arameter selection for multi-class r andom for est classification of cell nuclei
selected for further e xperiments in cell nuclei classification using random forests. Quantitati v e
e v aluation of classification performance of random forests sho ws the av erage o verall cross
v alidation accurac y as 64.01% and a v erage balanced cross v alidation accurac y as 63.99% .
T o summarize, it is observed that both the ensemble learning probabilistic methods ha v e a
superior classification performance o ver support v ector machines for the classification problem
of multiple cate gories of cell nuclei objects in the histopathological images. The performance of
the described methods is comparable to each other , with an ov erall precedence of random forests
as a result of highest a verage classification accuracies and a greater number of diagnostically
rele v ant classes achie ving better detection rates. Due to a satisfactory performance and higher
computational ef ficienc y , random forests machine learning method is selected for cell nuclei
classification in order to accomplish multiresolution enhancement of se gmentation, and determi-
nation of tissue composition in histological images. The ov erall achie v ed classification accuracies
are reasonable, ho we v er , not exceptionally superior , the prime reason being a dif ficult multi-class
problem at hand to classify the cell nuclei segments into one of the se ven defined classes, some of
which do not sho w consistent beha vior in visual appearance across the tissue images, especially
the tumor -af fected epithelial cells. The detailed results, observ ations and implications of cell
nuclei classification step are discussed in Section 8.3 . Also, the computational complexity and
associated implementation details of the described attrib ute calculation and machine learning
methods are summarized in Appendix A.5 .
6.4.3 Multir esolution Combination
T o combine the segmentation results of dif ferent magnifications, the procedure depicted in
Figure 6.14 is used. Firstly , the lo wer magnification (30
×
) contours are upscaled using nearest
neighbor interpolation [
Gonzales 2009
] to obtain cell nuclei objects and respecti v e set of 32 cell
nuclei features at the highest resolution. Next, the learning samples at the highest resolution
are used in a training procedure to learn random forest classifiers, which are then deployed for

94 CHAPTER 6. Stage 2: Image Pre-analysis
Fig. 6.14 Flowchart for multir esolution combination of cell nuclei se gmentation
T able 6.3 Relevance scor es assigned to classified cell nuclei objects
T ype of objects 30 × 40 ×
All cells 200 150
Conglomerates 100 100
Fragments 80 50
Artefacts 0 0
classification of unkno wn cell nuclei objects from the tw o resolutions. Follo wing the automatic
classification, a rele v ance score is assigned to each classified cell nuclei object depending on
its predicted class. This scoring is relati ve, depending on which information is visually more
significant for diagnostic purpose and for subsequent analysis of cancer regions, as gi v en in
T able 6.3 . The cell nuclei and fragments at 30
×
magnification are gi v en a higher rele v ance
than the corresponding se gments at 40
×
, due to a better visual quality at 30
×
and to reduce
o verse gmentation which is mainly found at 40
×
objecti v e magnification. No rele v ance is gi ven
to artefacts and the y are completely eliminated from the combined results as desired.
A ne w se gmentation map is created at the highest resolution where cell nuclei se gments from
indi vidual constituent magnifications are added according to their visual importance. Hence, by
using the classification results of se gments at dif ferent magnifications, a more accurate combined
se gmentation result is obtained, containing more useful information than indi vidual ones. The
approach can potentially lead to enhancement of cell nuclei se gmentation results from indi vidual
magnifications.
The outcome of multiresolution combination is quantitati v ely e v aluated using the method
described in Section 6.3.2 . The o verall combi nation result is summarized in Figure 6.15 .
From the quantitati v e and visual e v aluation of the se gmentation results after multiresolution
combination, it can be observed that these are superior to indi vidual magnifications 30
×
and
40
×
, with desired characteristics of both indi vidual magnifications. Correctly detected cell nuclei
se gments are almost maintained to the percentage in 40
×
magnification which has the highest
quantity . Overse gmentation i.e . the percentage of se gments not cell nuclei is reduced from 40
×

6.5 Applications 95
Fig. 6.15 Cell nuclei se gmentation performance after multir esolution combination
magnification by
≈ 4
%, and clustering i.e . the percentage of conglomerates is also reduced from
30
×
magnification by
≈ 1 . 5
%. By visual assessment of combined results, we find that some
cell nuclei fragments of 40
×
ha v e been replaced with corresponding whole cell nuclei of 30
×
.
Similarly , some conglomerates of 30
×
are replaced with indi vidual nuclei of 40
×
. Hence, the
multiresolution se gmentation result combines the characteristics at indi vidual magnifications.
Ho we ver , one ef fect to be carefully analyzed is the slight reduction in the number of correctly
detected cell nuclei compared to 40
×
magnification, as theoretically , should be higher than
indi vidual magnifications to realize the true adv antage of multiresolution approach by combining
their indi vidual contrib utions. A prime reason for this observ ation is misclassification during the
cell nuclei classification stage, which leads to a fe w higher precedence se gments being replaced
by other segments ( e .g . fragments) that are misclassified, resulting in decline in the number of
correctly detected nuclei. So the combined result is closer to
40 ×
, b ut a better version due to
lo wer o v ersegmentation. Hence, it can be concluded that the multiresolution combination has
caused an enhancement of segmentation properties in histopathological images of g astric cancer
and a more meaningful se gmentation has been achie v ed, containing diagnostically rele v ant visual
information compared to indi vidual magnifications.
6.5 A pplications
6.5.1 A utomatic Necrosis Detection
The primary requirement of the pre-analysis step is a high algorithmic performance and ef ficiency
with lo wer time and space requirements, which has been fulfilled using textural features and
SVM classification with discriminati v e thresholds (details of computational requirements in
Appendix A.5 ). Hence, this method is retained and applied for classification of unknown

96 CHAPTER 6. Stage 2: Image Pre-analysis
(a) (b) (c)
Fig. 6.16 Results for application of automatic necr osis detection on gastric cancer WSI datasets in (a)
HER2 positive tumor (b) HER2 ne gative tumor (c) Non-tumor .
histological re gions as necrotic or non-necrotic, using the labeled images from the training
datasets. For the studied WSI da tasets, the result of necrosis detection after applying the
automatic process is sho wn in Figure 6.16 . As expected, the percentage of necrotic areas is found
lo wer than non-necrotic areas in the three malignanc y regions, which coincides with pathologists’
observ ations during creation of ground truth. It also corresponds with the underlying diagnostic
hypothesis according to which the tumor is in the early stages, hence, necrosis is not widespread
in the tissue. The resulting necrosis classified regions are e xcluded from subsequent image
analysis tasks in the research pipeline.
Therefore, necrosis detection can be a useful preparatory step for pathologists and automatic
analysis tools for accurate disease observ ation and characterization. The proposed computer -
based method is a promising tool to detect necrosis in heterogeneous haematoxylin and eosin
stained whole slide images with v ariation in malignanc y le v els, stain intensity and biological
characteristics among patients. The experimental results on the g astric cancer WSI datasets
are encouraging and sho w rob ustness of the method to v arying visual appearances. The nov el
proposed necrosis detection application can act as a standalone program or as a pre-analysis step
in digital histopathology .
6.5.2 A utomatic Determination of Histological Tissue Composition
Histological composition of tissues for diagnostic purpose is currently determined by pathologists
using visual inspection in routine and research, which is a tedious and time-consuming process.
The cell nuclei classification step can be used as an automatic application to determine histo-
logical composition of gastric cancer tissues. Such tissue composition analysis can potentially
assist pathologists in computer -assisted diagnosis of gastric cancer . The method also provides a
basis for automatic dif ferentiation between tumor and non-tumor compartments of the tissue and
determination of cancer type, grade or extent (also considered for this w ork in Chapter 7 ). In this

6.5 Applications 97
section, a prototype application for automatic determination of histological tissue composition is
described and its preliminary results are illustrated using a pilot dataset, which can be e xtended
to lar ger datasets consisting of non-ov erlapping tiles from lar ger re gions of interest in the H&E
stained WSI.
Cell nuclei classification has been comparati v ely analyzed for three machine learning meth-
ods in Section 6.4.2 , and random forests are selected for classifying unkno wn cell nuclei samples.
The initial working dataset consisting of 75 image tiles is first used to create an enhanced
se gmentation result using the multiresolution combination approach, including automatic classi-
fication of unlabeled cell nuclei se gments using random forest classifiers. The combined result is
quantitati v ely analyzed to determine the proportion of each cell category in the three types of
malignanc y regions, namely , HER2 positiv e tumor , HER2 ne gati v e tumor and non-tumor . This
information can pro vide diagnostically rele v ant cues for subsequent visual or computational
analysis.
(a) (b)
(c)
Fig. 6.17 Example r esults of pr ototype application for automatic determination of tissue composition in
(a) HER2 positive tumor (b) HER2 ne gative tumor (c) Non-tumor .

98 CHAPTER 6. Stage 2: Image Pre-analysis
The measured tissue composition for the three types of re gions is sho wn in Figure 6.17 .
It can be observ ed that the two type of tumors ha v e similar tissue compositions, which are
visibly dif ferent from non-tumor . HER2 positi ve areas consist of the highest ratio of cell nuclei
fragments, epithelial cells and fibroc ytes and lo west percentage of leuk ocytes compared to other
two types of re gions, as expected. Also, HER2 neg ati v e regions ha v e intermediate properties
in terms of percentage of epithelial cells, leukoc ytes, fibroc ytes and fragments, and consist of
lar gest amount of conglomerates among the three malignanc y types. Non-tumor , in contrast, has
the maximum proportion of leukoc ytes and lo wer quantities of cell nuclei fragments, epithelial
cells, fibroc ytes and conglomerates.
Therefore, the automatically determined tissue composition resonates with the tissue com-
position e xpected pathologically in the corresponding malignancy areas. The results are also
in synchronization with the actual composition of the labeled data comprising part of initial
working datasets at 40
×
magnification, sho wn in Figure 5.10 (b). A fe w dif ferences arise due to
the error in the classification of cell nuclei, and multiresolution combination, leading to lo wer
fragment and conglomerate percentages in the resulting se gmentation.
6.6 Summary
In this chapter , the second stage of the proposed frame w ork, including the necessary image
pre-analysis methods, is defined. The stage incorporates the essential steps for processing
the current image datasets as required for fa vorable image analysis of g astric cancer . Firstly
automatic detection and e xclusion of necrotic areas is performed such that the final datasets
consist of non-necrotic re gions. Moreov er , a cell nuclei se gmentation algorithm is applied,
e v aluated and se gmentation results are enhanced using a no vel multiresolution combination
approach. This stage also contrib utes to wards tw o applications, namely , automatic necrosis
detection and automatic determination of histological tissue composition in microscopic images
using supervised learning and classification methods. The result of this stage is attainment of
necrosis-free histopathological image data consisting of additional information from the isolated
cell nuclei se gments for subsequent analysis of cancer regions.

C H A P T E R 7
Stage 3: Analysis of Cancer Regions
Contents
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
7.2 Ground T ruth Dataset Generation . . . . . . . . . . . . . . . . . . . . . . 101
7.2.1 Datasets for T raditional Machine Learning Methods . . . . . . . . . 101
7.2.2 Datasets for Deep Learning Methods . . . . . . . . . . . . . . . . . 102
7.3 Image Description Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 105
7.3.1 Lo w-le v el State-of-the-art Features . . . . . . . . . . . . . . . . . . 105
7.3.2 High-le v el State-of-the-art Features . . . . . . . . . . . . . . . . . . 107
7.3.3 High-le v el Handcrafted Features . . . . . . . . . . . . . . . . . . . . 108
7.3.4 Combination of Features . . . . . . . . . . . . . . . . . . . . . . . . 121
7.4 Machine Learning Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 123
7.4.1 T raditional Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 123
7.4.2 Deep Con volutional Neural Networks . . . . . . . . . . . . . . . . . 125
7.4.3 Classification Strategies . . . . . . . . . . . . . . . . . . . . . . . . 135
7.5 A pplications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
7.5.1
Computer -aided Diagnosis: Cancer Classification based on Immuno-
histochemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
7.5.2 Content-based Image Retriev al . . . . . . . . . . . . . . . . . . . . . 137
7.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
99

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Why institutions use Plag.ai for originality review, entry 73

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