Neu omuscula disease classi ica ion sys em
Au o a Sáez,aBegoña Acha,aAdo ación Mon e o-Sánchez,bEloy Ri as,cLuis M. Escude o,band Ca men Se anoa
aUni e si y o Se ille, Depa men o Signal Theo y and Communica ions, ETSI, 41092, Se ille, Spain
bRocío/CSIC/Uni e sidad de Se illa, Hospi al Uni e si a io Vi gen del, Ins i u o de Biomedicina de Se illa, 41013, Se ille, Spain
cHospi al Uni e si a io Vi gen del Rocío, Depa men o Pa hology, 41013, Se ille, Spain
Abs ac . Diagnosis o neu omuscula diseases is based on subjec i e isual assessmen o biopsies om pa ien s by
he pa hologis specialis . A sys em o objec i e analysis and classi ica ion o muscula dys ophies and neu ogenic
a ophies h ough muscle biopsy images o luo escence mic oscopy is p esen ed. The p ocedu e s a s wi h an
accu a e segmen a ion o he muscle ibe s using ma hema ical mo phology and a wa e shed ans o m. A ea u e
ex ac ion s ep is ca ied ou in wo pa s: 24 ea u es ha pa hologis s ake in o accoun o diagnose he diseases
and 58 s uc u al ea u es ha he human eye canno see, based on he assump ion ha he biopsy is conside ed as a
g aph, whe e he nodes a e ep esen ed by each ibe , and wo nodes a e connec ed i wo ibe s a e adjacen . A
ea u e selec ion using sequen ial o wa d selec ion and sequen ial backwa d selec ion me hods, a classi ica ion
using a Fuzzy ARTMAP neu al ne wo k, and a s udy o g ading he se e i y a e pe o med on hese wo se s o
ea u es. A da abase consis ing o 91 images was used: 71 images o he aining s ep and 20 as he es . A clas-
si ica ion e o o 0% was ob ained. I is concluded ha he addi ion o ea u es unde ec able by he human isual
inspec ion imp o es he ca ego iza ion o a ophic pa e ns.©2013 Socie y o Pho o-Op ical Ins umen a ion Enginee s (SPIE) [DOI: 10
.1117/1.JBO.18.6.066017]
Keywo ds: segmen a ion; wa e shed; uzzy classi ica ion; ea u e ex ac ion; neu omuscula disease; g aph heo y.
Pape 130052R ecei ed Jan. 30, 2013; e ised manusc ip ecei ed May 13, 2013; accep ed o publica ion May 20, 2013; published
online Jun. 26, 2013.
1 In oduc ion
Neu omuscula diseases co e a la ge g oup o pa hologies wi h
a huge he e ogeneous e iology and cou se. Al hough all neu o-
muscula diseases a e p og essi e in na u e, hey mani es in a
wide age ange wi h di e en deg ees o se e i y. The mos
common classi ica ion elies on he componen o he neu o-
muscula sys em ha is a ec ed. Hence, we can ind myopa-
hies, in which he muscle is p ima ily a ec ed, o
neu ogenic a ophies (NA), in which he mo o neu on is
a ec ed. To s udy he pa ien a ec ions, he pa hologis exam-
ines muscula biopsies unde mic oscope ocusing in o a mo -
phological muscula ibe analysis. The e alua ion o he
changes in he mo phological cha ac e is ics o a gi en biopsy
wi h espec o he no mal muscle is one o he main ea u es o
he diagnos ics o a neu omuscula disease. Howe e , he
manual mo phome ic app oach and in e p e a ion o muscle
biopsy ma e ial is a subjec i e, edious, and ime-consum-
ing ask.1
Cu en ly, a issue his opa hology slide can be digi ized and
s o ed in digi al-image o m. This p og ess has allowed he
de elopmen o compu e -assis ed diagnosis o disease de ec-
ion, diagnosis, and p ognosis p edic ion o complemen he
opinion o he pa hologis .2In his sense, in he ecen li e a u e
wo ks ela ed o g ading o p os a e cance ,3de ec ion o ce i-
cal cance ,4classi ica ion o hepa ocellula ca cicoma,5de ec-
ion o ce ical cell nuclei,6o simple de ec ion o di e en
ypes o cells7–9can be ound. Focusing on he s udies o
muscula ibe s, we can ind some wo ks ha add ess he seg-
men a ion o ibe s in muscula biopsies,10–12 he classi ica ion
o muscle- ibe ype,13–16 and he ex ac ion o mo phome ic
ea u es.17 Howe e , s udies o he cha ac e iza ion o neu o-
muscula disease based on image p ocessing ha e no been
ound in he cu en li e a u e.
This pape p esen s an analysis o luo escence mic oscopy
images o muscula biopsy o ob ain use ul in o ma ion o he
diagnosis and he se e i y g ading o di e en neu omuscula
diseases. To achie e his, ou da abase consis s o 91 images
belonging o con ol biopsies (no disease), biopsies a ec ed
by muscula dys ophies (MD), and biopsies a ec ed by NA.
The s udy is based no only on he ex ac ion o ea u es ela ed
o he cha ac e is ics ha he pa hologis akes in o accoun o
diagnosis, bu also on he sea ch o ea u es wi h inhe en p op-
e ies ha escape he e alua ion o he pa hologis and which
could be mo e e icien o he classi ica ion o he di e en
muscula images. In his sense, he pape p oposes an ex ac ion
o mo phome ic and s uc u al in o ma ion based on he
assump ion ha he biopsy is conside ed as a g aph, whe e
he nodes a e ep esen ed by each ibe , and wo nodes a e con-
nec ed i wo ibe s a e adjacen . This was mo i a ed by he
wo k o Escude o e al.,18 in which he in oduc ion o a ne wo k
allowed one o desc ibe he epi helial o ganiza ion objec i ely.
To ge he ea u e ex ac ion, an accu a e segmen a ion was
equi ed. In his pape , ma hema ical mo phology and a wa e -
shed ans o m a e used o add ess his ask. A uzzy classi ica-
ion based on a neu al ne wo k a chi ec u e is p esen ed. Finally,
a s udy o he se e i y g ading is ca ied ou o analyze he
esul s. In Sáez e al.,19 some o hese esul s, analyzed om
he biological poin o iew using di e en aining da a se s,
a e p esen ed.
Add ess all co espondence o: Au o a Sáez, Uni e si y o Se ille, Depa men o
Signal Theo y and Communica ions, ETSI, 41092, Se ille, Spain. Tel: +34 954
486091; Fax: +34 95 448 7341; E-mail: [email p o ec ed] o Luis M.
Escude o, Rocío/CSIC/Uni e sidad de Se illa, Hospi al Uni e si a io Vi gen
del, Ins i u o de Biomedicina de Se illa, 41013, Se ille, Spain. Tel: +34
955923048; Fax: +34 95 461 7301; E-mail: [email p o ec ed] 0091-3286/2013/$25.00 © 2013 SPIE
Jou nal o Biomedical Op ics 066017-1 June 2013 •Vol. 18(6)
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The es o he pape is o ganized as ollows: he ype o
images and he neu omuscula diseases a e p esen ed in Sec. 2;
in Sec. 3 he p ocedu e ollowed is explained, which includes a
segmen a ion me hod, a ea u e ex ac ion s ep, a ea u e selec-
ion s ep, a classi ica ion, and he se e i y g ading. In u n, each
sec ion con ains a subsec ion o esul s. Finally, a discussion o
he esul s is p esen ed.
2 Muscle Biopsy Images and Neu omuscula
Diseases
The muscula ibe s a e o ganized in ascicles, which a e su -
ounded by a laye o connec i e issue named he pe imysium.
Be ween he ibe s wi hin a ascicle appea s he endomysium, a
mesh o loose connec i e issue composed o ine collagen and
e icula ibe s. Skele al muscles ibe s can be classi ied in o wo
main ypes: ype I o slow ibe s ( hey ha e a slow con ac ion
eloci y) and ype II o as ibe s ( as con ac ion eloci y).
They a e dis ibu ed in a diso de ed mosaic pa e n, and appea
wi h a simila size along he ascicles. The ans e sal sec ion o
a no mal muscle ep esen s he ibe s wi h a polygonal shape
su ounded by a hin mesh o collagen.20
Muscula biopsies we e p ocessed by he s anda d me hods
o eezing and cu ing wi h c yos a . The muscula ibe and he
collagen con en we e de ec ed by luo escence mic oscopy. The
an ibodies mouse an i-myosin hea y chain (slow), mouse an i-
myosin hea y chain ( as ), and abbi an i-collagen ype VI we e
used using a s anda d p o ocol o immunos aining. The sec ions
we e incuba ed wi h Alexa luo 488 and Alexa luo 568 sec-
onda y an ibodies. All he slides we e analyzed unde a luo es-
cence mic oscope (BX-61 Olympus wi h a DP70 came a) using
a me cu y lamp h ough a 470 o 490 nm o 560 o 579 nm
band-pass il e o exci e Alexa luo 488 o Alexa luo 568,
espec i ely. The s ained cells we e pho og aphed, and wo
high- esolu ion images (size o 4080 ×3072 pixels) we e
ob ained. A o al o 91 images om 70 muscle biopsies, s o ed
in he Tissue Bank o he Hospi al Uni e si a io Vi gen del
Rocío, Se ille, Spain, we e p ocessed.
An RGB image was c ea ed om he wo images ob ained.
The ed componen is he image ob ained when he biopsy is
exci ed a 560 o 579 nm [Fig. 1(b)], and he g een componen
is he image ob ained when he biopsy is exci ed a 470 o
490 nm [Fig. 1(c)]. Figu e 1(a) shows a sample o he esul ing
RGB image. Slow ibe s in a da k colo , as ibe s in a eddish
colo , collagen in a g eenish colo , and capilla ies as small da k
s uc u es among he collagen can be obse ed.
All o iginal images ha e he same esolu ion (4080×
3072 pixels). Howe e , he igu es shown in his pape ep esen
only a pa o he comple e image (1150 ×1150 pixels) o co -
ec ly isualize he de ails. The yellow ba a he op o
Fig. 1(a) ep esen s he image scale co esponding o 200 μm.
In Fig. 1(d)–1( ), examples o he a iabili y in he images
a e shown. They belong o he pa ien s wi h di e en ages and
di e en diseases.
In his pape , h ee mo phological pa e ns p esen ed in
muscle biopsies a e s udied. The i s one is he no mal pa e n
(no disease). The second is he dys ophic pa e n. MD a e a ype
o myopa hy, which a e cha ac e ized by a wide a ia ion in
ibe size, a ounded shape o a ophic ibe s, and ib osis (an
inc ease o endomisial collagen). This con as s wi h he hi d
pa e n, NA ha p esen angula ed a ophic ibe s, la ge g oups
o a ophic ibe s, ascicula a ophy, and loss o he andom
Fig. 1 (a) Muscle biopsy image. The yellow ba a he op ep esen s he image scale. I co esponds o 200 μm, and i is he same o he es o he
images. (b) R-componen . (c) G-componen . (d) Example o muscle biopsy image a ec ed by neu ogenic a ophy (NA). (e) Example o muscle biopsy
(no disease) image belonging o a child. ( ) Example o muscle biopsy image a ec ed by muscula dys ophy (MD).
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checke boa d dis ibu ion o ibe ypes o ibe - ype g ouping.21
In addi ion, i is possible o ind a muscle ha can show a com-
bina ion o neu ogenic and myopa hic ea u es. Fo he s udy,
we ha e 91 images om 70 subjec s; 41 con ol images, 27 dys-
ophy images, and 23 a ophy images.
3 Me hodology
The mo phological analysis is a undamen al ool o he diag-
nosis o neu omuscula diso de s.22 The aim o his pape is o
analyze muscle biopsies in a mo phological and s uc u al way,
which allows one o de elop a diagnosis help ool o he neu o-
muscula diseases explained in he p e ious sec ion. The p o-
cedu e ollowed is desc ibed in Fig. 2.
Each block o he low diag am in Fig. 2is explained in he
ollowing sec ions.
3.1 Segmen a ion
A p e equisi e o classi y any disease is he abili y o au oma i-
cally iden i y he s uc u es p esen in he image.2Mo eo e , o
achie e a obus mo phological analysis, an accu a e segmen a-
ion o he ibe s in he biopsy image is equi ed. Shape desc ip-
ion and accu a e segmen a ion is possible i he ini ial
localiza ion o he muscle ibe s is known.23 The e o e, in
his pape he segmen a ion p ocess is di ided in o wo s eps:
iden i ica ion o he muscle ibe localiza ion by applying mo -
phological ope a o s, and accu a e de ec ion o he ibe con-
ou s by a wa e shed ans o ma ion. I is impo an o no e
ha he segmen a ion me hod mus be au oma ic and alid o
all ypes o images [see Fig. 1(a) and 1(d)–1( )].
3.1.1 Fibe localiza ion
The biopsy images we wo k wi h p esen di e en s uc u es
such as collagen, muscle ibe s, capilla ies, and a e ac s. The
aim o his s ep is he co ec iden i ica ion o he muscle ibe s.
The p ocessing is pe o med on he G-componen o he
image due o he high con as be ween he muscle ibe s and he
collagen [see Fig. 1(c)]. Conside ing ha he ibe s a e da ke
han he su ounding collagen, in ensi y alleys in he image a e
sea ched. Fo his eason, he H-minima ans o m24 is applied o
he G-componen image in o de o ge homogeneous minima
alleys. This ans o m has been success ully used in di e en
medical applica ions.25,26 The H-minima o H-maxima ans-
o m is a powe ul ma hema ical ool o supp ess undesi ed min-
ima o maxima. I allows one o ex ac egional minima whose
dep h is lowe han o equal o he gi en h- alue. Regional min-
ima a e connec ed componen s o pixels wi h a cons an in en-
si y alue, and whose ex e nal bounda y pixels all ha e a highe
alue. The H-minima ans o m24 is pe o med by
HhðGÞ¼Rε
GðGþhÞ;(1)
whe e h ep esen s he gi en dep h, and Rand ε ep esen he
econs uc ion and e osion ope a o s, espec i ely. The h- alue
has a di ec in luence on he numbe o segmen ed egions. The
la ge he h- alue, he ewe segmen ed egions. As he da kes
egions o he G-componen image ep esen he muscles ibe s,
we can in ui i ely assume ha he equi ed h- alue should be
lowe han he a e age in ensi y o he image. Th ee di e en
alues ha ook in o accoun he a e age in ensi y we e es ed:
wo hi ds o he a e age in ensi y o G(h1), hal o he a e age
in ensi y o G(h2), and one hi d o he a e age in ensi y o
G(h3). They a e calcula ed as
h¼K1
N·MX
N
i¼1
X
M
j¼1
Gði; jÞ;K¼2
3;1
2;1
3;(2)
whe e Gði; jÞis he in ensi y alue o G-componen a he pixel
ði; jÞand Nand Ma e he image pixel dimensions.
The esul ing image is a bina y image, in which egions wi h
a pixel alue o 1.0, displayed as whi e, ep esen candida e
muscle ibe s. Figu e 3shows he in luence o he h ee h- alues
on he numbe o segmen ed egions.
3.1.2 Resul s o he ibe localiza ion
Resul s we e es ed in only 10 1150 ×1150 pixel images. The
eason o his is ha he manual segmen a ion o each one is
necessa y in o de o check he quali y o he me hod, and
each image has hund eds o cells. So, he manual delinea ion
o hem is a e y ime-consuming and edious ask. I should
be no ed ha he numbe o cells segmen ed in hese es images
ep esen s only a po ion o he numbe o cells o he en i e
image. Table 1shows he numbe o cells segmen ed by he spe-
cialis , and he numbe o he egions de ec ed by he H-minima
ans o m wi h he h ee di e en h- alues. The numbe o
de ec ed egions should be close o he numbe o manually seg-
men ed cells, aking in o accoun ha he numbe o he de ec ed
egions mus be highe han o equal o he numbe o segmen ed
cells. I he alue is lowe , i will in ol e he loss o
de ec ed cells.
Al hough h1p o ides a numbe o de ec ed egions mo e
simila o he numbe o cells es ima ed by he specialis (see
Table 1), in some cases he numbe o de ec ed egions is
lowe . This means ha some cells a e no de ec ed. Since he
numbe shown he e ep esen s only a po ion o he numbe
o cells in he en i e image, his numbe o los cells could
inc ease. Fu he mo e, he o e segmen a ion is due o he exis -
ence o a e ac s and capilla ies in he image. These egions can
be emo ed by using mo phological ope a o s. Fo hese
Fig. 2 Flow diag am o he sys em.
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easons, he chosen h- alue is h2 ollowed by a p ocessing o he
esul ing image using ma hema ical mo phology.
The mo phological ope a o s used in his s ep a e summa-
ized he e. Regions wi h an a ea smalle han 0.25% o he
bigges dimension in pixels o he o iginal image ( aking in o
accoun he esolu ion indica ed in Sec. 2) we e emo ed by
applying mo phological opening.24 P esumably, small egions
co espond o capilla ies. I egula i ies (holes) wi hin he
egions de ec ed we e e illed by mo phological econs uc-
ion.24 Finally, an e osion ope a o wi h a 3×3 pixel s uc u al
elemen was applied o p e en ha adjacen cells we e joined.
The esul s o he numbe o egions de ec ed a e his s ep a e
shown in he ou h column o Table 2.
Al hough he numbe o egions dec eased, i could be desi -
able o be e adjus he numbe o de ec ed cells and he numbe
o segmen ed cells. To his aim, inally, wo independen colo
condi ions we e imposed on he de ec ed egions in o de o
emo e hose ha did no uly co espond wi h cells.
Fi s condi ion: hose egions whose a e age in ensi y o
G-componen had a alue highe han 25% o he maximum
in ensi y alue o Gin he da abase we e ejec ed as candida e
ibe s, as he muscle ibe s p esen a low g een alue.
Second condi ion: a his og am equaliza ion o he G-compo-
nen was pe o med, he a e age in ensi y o his new image was
calcula ed o all egions, and he maximum o hese alues was
compu ed. Finally, hose egions whose a e age in ensi y was
highe han he 90% o he maximum and an a e age alue
o R-componen less han 20% o he maximum in ensi y
alue o Rin he da abase we e emo ed.
The h esholds men ioned we e expe imen ally ixed, and
p o ide a co ec segmen a ion o he 91 images analysed.
Wi h hese wo condi ions, capilla ies and a e ac s p esen
among he collagen a e emo ed. The i h column o Table 2
p esen s he inal esul s.
3.1.3 De ec ion o ibe con ou s
Once he localiza ions o he muscle ibe s a e iden i ied, an
accu a e de ec ion o hei con ou s is equi ed o a la e obus
mo phological analysis. Fo his objec i e, wo well-known
echniques we e applied: le el se and a wa e shed ans o m.
Bo h me hods a e b ie ly explained below.
Le el se me hods27 ha e been widely used as a global
app oach owa d he op imiza ion o ac i e con ou s o he
Table 1 Numbe o manually segmen ed cells and numbe o 5
de ec ed egions by H-minima ans o m wi h di e en h- alues.
Image Ncells manual h1h2h3
17776 79 83
2 103 108 128 144
3 40 250 295 366
4 45 256 530 1000
5 56 371 467 574
6 40 61 113 245
7 92 90 105 121
84948 56 60
9 310 377 419 483
10 63 136 155 184
No e: The bold alues indica e ha numbe o he egions de ec ed is
lowe han numbe o manually segmen ed cells, his will in ol e loss o
de ec ed cells.
Table 2 Numbe o manually segmen ed cells and numbe o
de ec ed egions by H-minima ans o m wi h h- alue ¼hal o he
a e age in ensi y o G, a e he applica ion o mo phological ope a o s
and wo colo condi ions.
Image
Ncells
manual h2
Mo phological
ope a o s
Colo
condi ions
1777977 77
2 103 128 103 103
3 40 295 59 40
4 45 530 104 48
5 56 467 59 56
6 40 113 47 41
7 92 105 91 91
8495652 49
9 310 419 333 310
10 63 155 74 65
Fig. 3 (a) Muscle biopsy image. (b) H-minima ans o m wi h h- alue ¼h1(h1¼60.85). (c) H-minima ans o m wi h h- alue ¼h2(h2¼45.63). (d) H-
minima ans o m wi h h- alue ¼h3(h3¼30.42).
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segmen a ion o objec s o in e es om he backg ound. In he
li e a u e, nume ous wo ks ela ed o his echnique ha e been
p oposed. In his pape , he me hod de eloped by Li e al.28 is
used. In he epo ed wo k, an ene gy unc ion ies o main ain
he le el se unc ion nea he signed dis ance unc ion, hus
a oiding he need o e-ini ializa ion o he le el se unc ion.28
The ini ial cu e equi ed is he con ou o he bina y image
esul ing om he muscle ibe localiza ion de ec ion, explained
in he p e ious sec ion.
In he wa e shed p ocedu e,29 an image is iewed as a opo-
g aphic su ace: he highe he alue o a pixel, he highe he
al i ude a he co esponding poin on he opog aphic su ace o
elie . The wa e shed ans o m is usually applied o he g adien
image. The minima in he g adien image will co espond o he
si es wi hin homogeneous egions in he o iginal image.
Howe e , he wa e shed algo i hm yields esul s wi h subs an ial
o e segmen a ion; ha is, he numbe o segmen ed egions
could be much la ge han desi ed, wi h egions being b oken
in o mul iple smalle egions. This undesi able esul is due
o he ac ha he g adien image used in he p ocess is sensi i e
o noise. The p oblem o o e segmen a ion can be o e come
wi h he use o ma ke s ha iden i y he objec s. The objec con-
ou s in he g adien image can be seen as he highes c es -lines
a ound he objec ma ke s. In ou case, he image g adien is
calcula ed in he G-componen , and he in e nal and ex e nal
ma ke s a e de i ed om he p e ious sec ion, whe e he aim
was o iden i y he muscles ibe s. The bina y image esul ing
om he p e ious sec ion cons i u es he in e nal make s.
Meanwhile, he ex e nal ma ke s we e ex ac ed by applying
ano he wa e shed ans o m o he bina y image used as in e nal
ma ke s.
3.1.4 Resul s o he de ec ion o he ibe con ou s
To e alua e he pe o mance o bo h me hods, 10 images man-
ually segmen ed by he specialis we e used. The esul s we e
e alua ed wi h he Jacca d coe icien and he Dice coe icien .
The Jacca d index, also known as he Jacca d simila i y coe i-
cien , is a s a is ical pa ame e used o compa ing he simila i y
and di e si y o sample se s. I is de ined as he size o he in e -
sec ion di ided by he size o he union o he segmen ed cells.
Assuming wo se s co esponding o he segmen ed pixels
ob ained om he manual segmen a ion (Pm) and he segmen ed
pixels ob ained om he au oma ic me hod (Ps), Jacca d’s coe -
icien is de ined as
J¼jPm∩Psj
jPm∪Psj:(3)
The Dice coe icien , D, is also a simila i y measu e, which is
de ined as
D¼2jPm∩Psj
jPmjþjPsj:(4)
Resul s a e p esen ed in Table 3. As can be seen, he wa e -
shed ans o m ou pe o ms le el se echnique in all images.
Some image examples o bo h segmen a ions a e shown in
Fig. 4. I is impo an o no e ha besides he accu acy in
de ec ing he con ou s o he ibe s is highe in wa e shed
[Fig. 4(b)] han in le el se s [Fig. 4(c)], he wa e shed ans o m
is able o sepa a e wo independen cells al hough hey seem o
be linked (see yellow ec angles).
Table 3 Segmen a ion esul s o he wa e shed ans o m and le el se
segmen a ion e alua ed by he Dice coe icien and he Jacca d
coe icien .
Image
Dice coe icien Jacca d index
Wa e shed Le el se s Wa e shed Le el se s
1 0.963 0.94 0.927 0.8969
2 0.966 0.93 0.933 0.868
3 0.97 0.95 0.942 0.912
4 0.969 0.952 0.939 0.908
5 0.969 0.949 0.94 0.903
6 0.956 0.938 0.915 0.881
7 0.96 0.917 0.922 0.843
8 0.975 0.947 0.951 0.89
9 0.971 0.917 0.94 0.84
10 0.973 0.922 0.948 0.854
A e age 0.967 0.936 0.934 0.879
No e: The bold alues indica e he bes esul .
Fig. 4 (a) Muscle biopsy image. The yellow ec angle indica es linked cells. (b) Wa e shed segmen a ion esul . The yellow ec angle indica es a good
esul . (c) Le el se segmen a ion esul . The yellow ec angle indica es a bad esul , because wo linked cells ha e been segmen ed as only one.
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Fu he mo e, he compu a ional cos is also lowe o wa e -
shed. Figu e 5shows ha he a io o compu a ional ime
be ween bo h segmen a ions (consumed ime by le el se s/con-
sumed ime by wa e shed) inc eases loga i hmically wi h he
numbe o cells o segmen in he image. Fu he mo e, i should
be no ed ha he ime spen on segmen ing he images by he
p oposed me hod is signi ican ly less han ha needed by he
specialis (see Table 4). The s eps ollowed in inal segmen a ion
p ocess a e shown in Fig. 6.
3.2 Fea u e Ex ac ion
To iden i y i a biopsy is a ec ed by a pa hology, an objec i e
analysis o he biopsy is needed. The mo phological and s uc u al
cha ac e is ics o he whole biopsy cons i u e a ec o , also called
biosigna u e.23 The objec i e o his sec ion is o ex ac he ea-
u es ha co espond o he isual a ibu es de ined by clinicians
as pa icula ly impo an o he men ioned pa hology g ading and
diagnosis as well as a s udy o new ea u es wi h inhe en p ope -
ies ha escape om he e alua ion o he pa hologis and ha
could be use ul o he cha ac e iza ion o hese diseases. In his
sense, bo h mo phological and s uc u al ea u es a e p oposed.
3.2.1 Mo phological ea u es
The mo phological cha ac e is ics can be desc ibed by shape o
geome y.23 Fo mula ion o mo phological ea u es is an easy
Fig. 5 Ra io be ween compu a ional cos o bo h segmen a ion me hods
and numbe o cells segmen ed.
Table 4 Time spen on segmen ing he images by he p oposed
me hod and by he specialis .
Image Manual segmen a ion (min) P oposed me hod (s)
120 33
2 33 35.5
314 33
4 22 33.7
5 11 30.14
68 30
729 41
815 34
940 45
10 20 33
A e age 21.2 31.5
Table 5 Fou een mo phological ea u es o he cells.
1 A e age a ea
2 S d. de . a ea
3 A e age a ea o slow cells
4 S d. de . a ea o slow cells
5 A e age a ea o as cells
6 S d. de . a ea o as cells
7 A e age majo axis
8 A e age mino axis
9 A e age a io axis
10 S d. De . a io axis
11 A e age con ex hull
12 S d. De . con ex hull
13 A e age angles
14 S d. de . angles
Fig. 6 S eps ollowed in he ibe segmen a ion.
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and as way o au oma ize he manual mo phological quan i i-
ca ion, which is e y labo ious and subjec i e.
Fea u es such as he a ea o each ibe , he a e age o he
a eas o ype I (slow) and ype II ( as ) ibe s, iden i ied by
he in ensi y a e age o R-componen , o he majo and he
mino axis leng hs o he cells a e calcula ed. In Table 5, he
14 cha ac e is ics compu ed a e shown.
3.2.2 S uc u al ea u es
To add ess his app oach, each biopsy image was in e p e ed as a
g aph. G aphs a e e icien da a s uc u es o ep esen spa ial
da a, and an e ec i e way o ep esen s uc u al in o ma ion
by de ining a la ge se o opological ea u es.2Fo mally, a sim-
ple g aph G¼ðV;EÞis an undi ec ed and unweighed g aph
wi hou sel -loops, wi h Vand Ebeing he node and edge
se o g aph G, espec i ely. Applying his concep o ou p ob-
lem, we gene a ed a cellula ne wo k in which he muscle ibe s
a e ep esen ed by nodes, and wo nodes a e connec ed i wo
ibe s a e adjacen .
To iden i y he neighbo hood o each ibe , we gene a ed a
mosaic such ha each ibe con ou is expanded o each he
expanded con ou o he adjacen ibe . This concep was
add essed by applying a wa e shed ans o m o a bina y
image esul ing om he ibe de ec ion s ep (Sec. 3.1.3). An
example is shown in Fig. 7(c).
I should be no ed ha an impo an ea u e was ex ac ed
om his mosaic. The a io be ween he a ea o a ibe (A2)
and he a ea when i s con ou is expanded (A1) ( ea u es 15
and 16 in Table 6) is indica i e o he amoun o he collagen,
and he e o e indica i e o he exis ence o ib osis, one o he
main a ibu es o he MD.
In his poin , a ec o o 24 ea u es (included he 14 geo-
me ical ones) was ixed. Cha ac e is ics such as he numbe o
neighbo s o he numbe o neighbo s o a de e mined ype o
ibe we e added. Table 6shows he added ea u es.
These 24 ea u es y o emula e he cha ac e is ics ha he
pa hologis akes in o accoun o diagnosis, howe e , in his
pape ano he se o ea u es ha escape om he e alua ion o
human ision is ex ac ed. Fo his pu pose, a weigh ed g aph
de i ed om he muscula biopsy was gene a ed, whe e each
node was ep esen ed by he mass cen e o he each ibe ,
and he neighbo hood ela ions a e mapped in o weigh ed
edges, whe e each weigh co esponds o he Euclidean dis ance
be ween he nodes [see Fig. 7(d)].
Fi y-eigh new cha ac e is ics we e inco po a ed. The
i s 14 cha ac e is ics ou o 58 ones we e ob ained om he
pa ame e s ha we e al eady compu ed (a ea, axis, con ex
hull, angles, and a io A1/A2), bu aking in o accoun he neigh-
bo hood o each ibe , i.e., he a io be ween he alue o he
pa ame e s o each ibe and he a e age o he alues o he
co esponding adjacen ibe s cons i u ing hese new 14 ea u es
( ea u es 25 o 38 in Table 7). The 44 emaining ea u es a e
compu ed om g aphs heo y ( ea u es 39 o 82 in Table 7)
when i is applied o an undi ec ed and weigh ed g aph.
Table 7shows hese 58 new ea u es.
To a oid e o s due o he lack o neighbo s o he ibe s a
he image edge, he cha ac e is ics a e calcula ed on a egion o
in e es (ROI) chosen by he use s, such ha a leas one ow o
ibe s a ound he ROI exis s [see Fig. 7(d)].
3.3 Fea u e Selec ion
Fea u e selec ion has wo bene i s: i educes he cos o
da a collec ion and compu a ional cos o ecogni ion, and i
usually imp o es he gene aliza ion pe o mance o he classi-
ie . Ac ually, a la ge se o ea u es may possibly be de imen al
o he classi ica ion pe o mance, a phenomenon known as “ he
cu se o dimensionali y.”Fea u e selec ion is a means o selec
he ele an and impo an ea u es om a la ge se o ea u es.
An op imal ea u e selec ion me hod would equi e an exhaus-
i e sea ch, which is no p ac ical o a la ge se o ea u es gen-
e a ed om a la ge da ase . The e o e, se e al heu is ic
Fig. 7 (a) Muscle biopsy image. (b) Mask o he wa e shed segmen a ion esul . (c) Mosaic, whe e each ibe con ou is expanded o each he expanded
con ou o he adjacen ibe . (d) G aph, whe e each node is ep esen ed by he mass cen e o each ibe , and he neighbo hood ela ions a e mapped
in o he edges.
Table 6 S uc u al ea u es.
15 A e age a io A1/A2
16 S d. de . a io A1/A2
17 A e age neighbo s
18 S d. De . neighbo s
19 S d. de . neighbo s o slow ibe s
20 S d. de . neighbo s o as ibe s
21 Slow neighbo s o slow ibe s
22 Fas neighbo s o slow ibe s
23 Slow neighbo s o as ibe s
24 Fas neighbo s o as ibe s
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algo i hms ha e been de eloped which use classi ica ion accu-
acy as he op imali y c i e ion.2
In his pape , he well-known ea u e selec ion me hods,
namely sequen ial o wa d selec ion (SFS) and sequen ial back-
wa d selec ion (SBS),30 a e used. SFS wo ks by sequen ially
adding he ea u e ha mos imp o es he classi ica ion pe o -
mance; simila ly, SBS begins wi h he en i e ea u e se and
sequen ially emo es he ea u e ha mos imp o es he classi-
ica ion pe o mance. While hese me hods s ill canno gua an-
ee op imali y o he selec ed ea u e subse , hey ha e been
shown o pe o m e y well compa ed wi h o he ea u e selec-
ion me hods31 and a e, u he mo e, much mo e compu a ion-
ally e icien .32
As i has been men ioned, hese me hods use classi ica ion
accu acy as he op imali y c i e ion. In his case, he classi ica-
ion was pe o med by a Fuzzy-ARTMAP neu al ne wo k. I is a
neu al ne wo k a chi ec u e de eloped by Ca pen e e al.,33 and
i is based on adap i e esonance heo y (ART). Fuzzy-
ARTMAP is a supe ised lea ning classi ica ion a chi ec u e
o analogue- alue inpu pai s o pa e ns, whe e each indi idual
inpu is mapped o a class label.
To e alua e he classi ica ion accu acy, a se o 71 images
was used. Thi y- ou con ol biopsies om quad iceps and
Table 7 Fi y-eigh new s uc u al ea u es.
25 A e age ela ion neighbo s a ea
26 S d. de . ela ion neighbo s a ea
27 A e age ela ion neighbo s majo axis
28 S d. de . ela ion neighbo s majo axis
29 A e age ela ion neighbo s mino axis
30 S d. de . ela ion neighbo s mino axis
31 A e age ela ion neighbo s ela ion axis
32 S d. de . ela ion neighbo s ela ion axis
33 A e age ela ion neighbo s con ex hull
34 S d. de . ela ion neighbo s con ex hull
35 A e age ela ion neighbo s angles
36 S d. de . ela ion neighbo s angles
37 A e age ela ion neighbo s a io A1/A2
38 S d. de . ela ion neighbo s a io A1/A2
39 A e age s eng hs
40 S d. de . s eng hs
41 A e age s eng hs o as cells
42 S d. de . s eng hs o as cells
43 A e age s eng hs o slow cells
44 S d. de . s eng hs o slow cells
45 A e age clus e ing coe icien
46 S d. de . clus e ing coe icien
47 A e age clus e ing coe icien o as cells
48 S d. de . clus e ing coe icien o as cells
49 A e age clus e ing coe icien o slow cells
50 S d. de . clus e ing coe icien o slow cells
51 A e age eccen ici y
52 S d. de . eccen ici y
53 A e age eccen ici y o as cells
54 S d. de . eccen ici y o as cells
55 A e age eccen ici y o slow cells
56 S d. de . eccen ici y o slow cells
57 A e age be weenness cen ali y
58 S d. de . be weenness cen ali y
59 A e age be weenness cen ali y o as cells
60 S d. de . be weenness cen ali y o as cells
61 A e age be weenness cen ali y o slow cells
62 S d. de . be weenness cen ali y o slow cells
63 A e age sho es pa hs leng hs
64 S d. de . sho es pa hs leng hs
65 A e age sho es pa hs leng hs om as cells o as cells
66 S d. de . sho es pa hs leng hs om as cells o as cells
67 A e age sho es pa hs leng hs om as cells o slow cells
68 S d. de . sho es pa hs leng hs om as cells o slow cells
69 A e age sho es pa hs leng hs om slow cells o slow cells
70 S d. de . sho es pa hs leng hs om slow cells o slow cells
71 A e age sho es pa hs leng hs om slow cells o as cells
72 S d. de . sho es pa hs leng hs om slow cells o as cells
73 Radius
74 Diame e
75 E iciency
76 Pea son co ela ion
77 Algeb aic connec i i y
78 S me ic
79 Asso a i i y
80 Densi y
81 T ansi i i y
82 Modula i y
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biceps, 20 biopsies images a ec ed by dys ophy, and 17 biop-
sies images a ec ed by NA. Th ee s udies we e ca ied ou :
•Compa ison be ween he h ee g oups o images [con ols
(C)−MD−NA]
•Compa ison be ween con ol and dys ophies (biopsies o
muscle a ec ed by dys ophy belong o quad iceps)
•Compa ison be ween con ol and NA (biopsies o muscle
a ec ed by a ophy belong o biceps)
Fo each compa ison, he selec ion pe o mance was e alu-
a ed by ou old c oss- alida ion (XVAL).30 In his sense, he
disad an age o sensi i eness o he o de o p esen a ion o
he aining se ha he SBS and SFS me hods p esen was
diminished. To pe o m he XVAL me hod, ou disjoin subse s
o each class (con ol, dys ophy, NA) we e used. Th ee o hese
subse s se ed as aining se s o he neu al ne wo k, while he
o he one was used as a alida ion se . Then, he p ocedu e was
epea ed in e changing he alida ion subse wi h one o he
aining subse s, and so on, ill all ou subse s we e used as
alida ion se s. The inal classi ica ion e o was calcula ed as
he mean o he e o s o each XVAL un.
3.3.1 Resul s o ea u e selec ion
The ea u e selec ion p ocedu e was pe o med wice o he
h ee compa isons men ioned abo e. The i s selec ion was ca -
ied ou on he 24 i s ea u es desc ibed in he p e ious sec ion
(see Tables 5and 6), and he second selec ion was pe o med on
he 82 ea u es (see Table 7). The esul s a e shown in Table 8,in
which he selec ed ea u es and he classi ica ion e o ob ained
wi h his selec ion a e shown. The selec ed ea u es a e p e-
sen ed in ascending o de by disc imina ion powe .
As can be seen, when we compa e be ween bo h quad iceps
(dys ophies) and biceps (a ophies), he classi ica ion success is
100%, he e o e, adding new ea u es does no imp o e he clas-
si ica ion e o in his s age o aining. In he ollowing sec ion,
we will s udy wha classi ica ion e o is ob ained when we clas-
si y new biopsies, ha we e no included in he s age o ea u e
selec ion. Howe e , in he case o he dis inc ion be ween he
h ee ca ego ies, he classi ica ion e o dec eases when we
add s uc u al ea u es. E en so, in he nex sec ion we check
how hese se s o ea u es a e good o classi y new biopsies.
3.4 Classi ica ion
The ex ac ed ea u es ep esen he inpu s o a classi ica ion
p ocedu e. The classi ie used in his pape is a Fuzzy
ARTMAP neu al ne wo k.
The Fuzzy ARTMAP sys em inco po a es wo uzzy ART
modules, ARTaand ARTb, ha a e linked oge he ia an
in e -ART module, Fab, called a map ield (see Fig. 8).
In he p edic ion s age, “a”( ea u es) is he inpu ec o , and
i is hoped ha he sys em esponds wi h he “b” ec o (ca ego-
ies). In ou implemen a ion, Fa
2is composed by N:j’ h nodes
(j¼1;:::;N,N¼numbe o aining images) and Wab
j
deno es he weigh ec o associa ed o he j’ h node o Fa
2.
In he p edic ion s age, he jnode o Fa
2is ac i a ed h oughou
he maximum o a choice unc ion Tj.
The Wab
jassocia ed o his node ac i a es a Knode o Fb
2.
This Knode is he ca ego y chosen ha he sys em p edic s is
associa ed o he “a”inpu . Howe e , in ou implemen a ion, he
ca ego y choice is modi ied. As each jnode in Fa
2is associa ed
o a aining image, we ha e aken in o accoun he a e age o
he alues Tjby ca ego ies ins ead o he maximum. Le cl be
he ca ego y (cl ¼1;:::; M,M¼numbe o he ca ego ies), in
ou case, cl ¼1 ep esen s con ol, cl ¼2 ep esen s dys ophy,
and cl ¼3 ep esen s a ophy, espec i ely. Le ncl be he num-
be o he aining images o he cl ca ego y. The ca ego y choice
p ocedu e is:
o cl ¼1∶M
kcl (Tj), j¼1:ncl
End.
The ca ego y chosen ul ills:
KCL ¼max kcl∶cl ¼1∶Mg:(5)
Table 8 Fea u e selec ion esul s.
Compa ison
24 ea u es 82 ea u es
Selec ed ea u es Classi ica ion e o (%) Selec ed ea u es Classi ica ion e o (%)
Con ols-dys ophies (C/MD) 19 18 15 0 25 9 0
Con ols-a ophies (C/NA) 12 20 21 22 0 34 16 21 45 0
Con ols-dys ophies-
a ophies (C/MD/NA)
20 9 19 18 16 21 14 17 13.22 25 32 14 16 58 62 15 30 26 1.48
Fig. 8 Fuzzy ARTMAP a chi ec u e.
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