M7.8
TEMPORAL AUTOMATIC EDGE DETECTION
OF
ECHOCARDIOGRAPHIC IMAGES
L. To es, A. Gasull
Dep . o Signal Theo y and Communica ions
E.T.S.I. Telecomunicaci6n
-
UPC
Apdo. 30.002
08080
BARCELONA, SPAIN
ABSTRACT
This pape p esen s a new ully au oma ic algo i hm ha
ex ac s le en icula epica dial and endoca dial bo de s om
2D echoca diog ams.
The algo i hm s a s wi h he end sys ole and end dias ole
con ou s ob ained h ough a adial analysis o he image. A e
some i e a i e app oxima ions, excellen quali y con ou s o
s a ic images
a e
ound. A his poin , in o de o ake ad an age
o he in o ma ion con ained in he empo al domain, he
echnique is applied o he whole ca diac cycle hus ob aining a
comple e se ies o ca diac con ou s ha a e used in a u he
s age o md he le en icula chambe olume and some o he
ela ed pa ame e s. The algo i hm has been applied o di e en
ca diac iews, such a pa as e nal sho axis, apical and mi al,
wi h he same deg ee o success.
I. INTRODUCTION
Du ing he las i een yea s echoca diog aphy has
expe ienced widesp ead accep ance
in
he e alua ion o ca diac
disease. This is in la ge pa due o i s nonin asi e na u e and
i s eal ime capabili y o obse ing bo h ca diac s uc u e and
mo ion. Two-dimensional echoca diog aphy is capable o
p o iding an in eg a ed o e all iew o bo h ca diac s uc u e
and mo ion. Since bo h global and egional le en icula
unc ion a e majo a iables used o de e mine p ognosis in
ca diac disease, he e is a g ea deal o in e es
in
abili ies o
quan i a e unc ion indexes om a ious ypes o ca diac
imaging echniques. Fo ins ance, he analysis o a comple e
cycle o ca diac images, allows o ind le en icula chambe
olume
and
le en icula ejec ion ac ion.
Compu e s ha e been used mainly o assis in inding
ca diac pa ame e s.
In
his ole, hey ha e been adap ed in a
simple ashion, i.e., manual bo de s a e de ined using some
ype o en y sys em, and subsequen a ea and pe ime e
calcula ions a e made [l], [2]. Some a emp s o in oduce he
compu e in he decision-making p ocess ha e been p esen ed
wi h a mode a e amoun o success. Fo ins ance, Zhang and
Geise [3] ha e p oposed an image p ocessing algo i hm o
ex ac ing he endoca dial bo de om a se ies o 2-dimensional
echoca diog ams (2D echo). Thei me hod is a e y e icien
me hod o con ou ex ac ion o echog aphic images bu
some ained ope a o has o manually d aw he end sys ole and
end dias ole con ou s. Besides, he algo i hm ails o de ec
con ou s when he ca i y is
no
noise- ee.
In
his con ex , he e
is a need o e icien me hods o noise emo al and pos e io
pa ame e ex ac ion.
This wo k has been suppo ed by
NTE,
S.A. unde Eu opean
Space Agency con ac 7320/87/NLPP
A main objec i e o digi al p ocessing
o
echoca diog aphic
images is o imp o e he signal- o-noise a io
o
he ideo
images acqui ed om he ul asound equipmen .
In
addi ion o
he whi e Gaussian noise associa ed wi h ideo signals, he e is
a la ge amoun o acous ic speckle p oduced by ue signal ha
a i es a he ansduce , bu is no associa ed wi h e um echo
om he hea .
So,
a p ime goal is he emo al o speckle noise
o enhance he image in o de o acili a e bo h isual inspec ion
o he hea and compu e pa ame e ex ac ion. A e iew o
classical me hods o le en icula con ou ex ac ion and
p ep ocessing o echoca diog aphic images is p esen ed
in
[4].
Once he image has su e ed a smoo hing p ocess, a con ou
ex ac ion algo i hm may be applied in o de o ex ac ca diac
pa ame e s. We ha e de eloped a comple ely ully au oma ic
algo i hm o ha pu pose. The algo i hm s a s wi h he end
sys ole and end dias ole ob ained con ou s. The main di e ence
wi h he Zhang and Geise me hod is ha hese i s con ou s
a e ob ained au oma ically. In o de o ob ain he wo i s
con ou s, he image in o ma ion is changed o pola coo dina es
and a adial analysis o he image is ca ied ou . Th ough some
i e a i e app oxima ions, excellen quali y con ou s a e
ob ained. A his poin , in o de o ake ad an age o he
in o ma ion con ained in he empo al domain, he echnique is
applied o he whole ca diac cycle hus ob aining a comple e
se ies o ca diac con ou s ha a e used in a u he s age o ind
he le en icula chambe olume and some o he ela ed
pa ame e s.
Tempo al p ocessing has been applied due o he ac ha
ul asound images con ain a lo o co ela ion hus allowing
us
o use use ul echniques o dec ease he sea ch window and
speeding up compu a ions. The algo i hm has been applied o
di e en ca diac iews, such as pa as e nal sho axis, apical
and mi al, wi h he same deg ee o success.
11. STATIC ALGORITHM
As i has been said he s a ing images o he algo i hm
a e
he end sys ole and end dias ole con ou s.
In
o de o ind hese
iis con ou s we ha e de eloped an au oma ic me hod which
has been al eady epo ed [S].The i s s ep is o ind he
coo dina e cen e in
he
in e io o he ca i y whe e wall
con ou s a e being sea ched. Then he image is con e ed in o
pola coo dina es by calcula ing I(kpo,l~o) om I(nxo,myo).
Bilinea in e pola ion has been used. Once he image is in pola
o m, he
so
called dis ance unc ion is ound, by de ining some
special cha ac e is ic ( i s maximum, maximum alue, e c.) o
each adius and d awing he esul ing unc ion. A di e en
dis ance unc ion is e alua ed o each con ou .
Fig.
1 shows
he s a ing unc ion o he inne con ou (endoca dium) whe e
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CH2847-2/90/0000-2149
$1.00
0
1990
IEEE
maximum alue o each adius has been used o de ine he
dis ance unc ion. The goal o he algo i hm is o ind he bes
possible dis ance unc ions o bo h he inne and ou e con ou s
om hese i s s a ing dis ance unc ions. Fig.
1
also shows
he inal con ou (dashed line).
'J
m
i7a
S'I
a
Fig.
1.
Ou e con ou dis ance unc ions. Con inuous line
:
Fi s
Once he i s app oxima ion o he dis ance unc ion is
ound, he algo i hm disc imina es which poin s o his unc ion
belong o he con ou and which do no . To do his, successi e
app oxima ions a e gene a ed un il he con ou , o a he i s
co ec dis ance unc ion is gene a ed. The i s s ep is o
de e mine, om he il e ed dis ance unc ion, a i s span o
wha can be de e mined o be he ac ual con ou . This i s span
will lead o ollow he con ou poin s. The p ocess o selec ing
he bes span is done h ough ex apola ion o each span along
he whole ange o he unc ion, and selec ing he span ha
gi es he leas squa e e o . The ex apola ion is pe o med by a
Fou ie se ies app oach, aking in o accoun ha he unc ion is
con inuous, pe iodic and wi h many con inuous de i a i es.
Th ough some i e a i e p ocess all co ec spans a e
inco po a ed. Fo he sake o comple eness we summa ize he
s a ic algo i hm in Fig.
2.
111. TEMPORAL ALGORITHM
app oxima ion. Dashed line
:
Final esul .
Once he end sys ole and end dias ole con ou s ha e been
ob ained h ough he s a ic algo i hm, i is logical o apply he
me hod o he whole se ies o empo al images. I knowledge o
he ca diac s uc u e, mo ion and ela ed pa ame e s is
inco po a ed,
i
will be possible o ind a as algo i hm o ind
he whole se ies o empo al con ou s.
The basic idea is o ind a me hod o con ou sea ching in
such a way ha s a ing wi h an app oxima e knowledge
o
he
posi ion o he eal con ou o e e y one
o
he images in a
empo al ul asound sequence, i allows o ob ain he comple e
se o eal con ou s.
The ac o knowing he app oxima e posi ion o he
con ou , allows o apply he eal con ou poin s iden i ica ion
c i e ia o a es ic ed a ea a ound he p e ious es ima ion o he
con ou posi ion.
In
his way he in e e ence due o o he
ca diac s cuc u es such as papilla y muscles, al es, o e en
ex emal con ou while he in emal con ou is being ob ained (o
ice e sa), is a oided du ing he p ocess
o
de ec ing he poin s
ha belong o he con ou wan ed o be ound. Sea ching in a
educed egion allows ha he p ocess o con ou de ec ion be
as e han
in
he case o s a ic images whe e all he poin s
o
he image need o be analized.
Inpu
IP ep ocessing
I
ICoo dina es cen e calcula ion]
I
Image ans o ma ion o pola coo dina es
I
/P ep ocessing
I
IDis ance unc ion compu a ion
I
IMedianFil e ing
1
[Selec ion o con inuous spans
I
Fou ie se ies app oxima ion o
o de
0
and
1
o each span
Mean
Squa e
e o compu a ion be ween
he dis ance unc ion and each app oxima ion
Selec ion o he bes span by leas
e o app oxima ion
Icon ou acking
I
lpoin elimina ion
I
IFou ie se ies app oxima ion
I
In e pola ion by cha ac e is ic
sea ching
In e pola ion by a g ay-le el
sea chinp.
I
Poin elimina ion
1
IFou ie se ies app oxima ion
I
I
Final
Con ou
I
Fig.
2.
Au oma ic con ou ex ac ion algo i hm o s a ic images
The i s s ep is o p ep ocess he image in o de o elimina e
he e ec s o he noise p esen
in
he images du ing he con ou
de ec ion. A e wa ds, he image is con e ed in o pola
coo dina es. The bes p ep ocessing esul s in sequen ial images
ha e been p o ided by a
5*5
empo al
/
spa ial median il e ing
o h ee consecu i e images in pola coo dina es.
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In
all he images o he sequence, he sea ch o he con ou
is s a ed based on an es ima ed posi ion o he eal con ou .
This posi ion is de e mined om
he
con ou s co esponding
o
he s a and end o he sequence
-
end dias ole and end sis ole-
and assuming ha he walls mo ion is lineal du ing he ca diac
cycle. The c i e ium o sea ching he con ou poin s is applied
o
poin s a ound o a speci ied cen e de e mined by he
es ima ed posi ion. This p ocess is applied o e e y adii o he
image ha has p e iously been con e ed in pola coo dina es.
The size o he sea ching egion is a pa ame e ha depends on
he image in such a way ha egions ha could lend o w ong
poin s de ec ions
-
papilla y muscles o he con ou s e c.
-
a e
a oided. The de ec ed poin s a e hen alida ed and om hese
poin s a i s app oxima ed con ou is buil . This i s con ou
will be he s a ing poin o a second sea ching p ocess. The
inal con ou is ob ained om he esul o his second sea ch
o e e y one o
he
images o he sequence.
The con ou s o end dias ole and end sis ole a e he
s a ing
poin o he inal p oposed algo i hm o empo al images.
These con ou s
a e
called:
R'd
i=l,
...,
N
Ris
i=i,
...,
N
whe e N is he numbe o adiis o he image o be p ocessed;
in his case N =
360.
These alues ep esen he dis ance
measu ed in pixels om he cen e o coo dina es o he image
o
he poin o he con ou o each adii i.
F om hese alues, he es ima ed con ou s o he es o he
images ( he whole empo al sequence)
a e
ob ained by means o
a lineal in e pola ion
in
he
ollowing way:
..
R'd-R',
RInd=
R,
-
~
x
(N-nd)
N
-N,
whe e ns
=
1
,
. . .
,
Ns.
nd
=
Ns+l
,
.
.
.
,
N.
N
=
Ns+Nd+l.
Ns is he numbe o images be ween he ins an s o end
dias ole and end sis ole, Nd is he numbe o images be ween
he ins an s o end sis ole and end dias ole and N is he o al
numbe o images in he conside ed sequence. The esul s o
his in e pola ion gi e he es ima ed con ou s o e e y one o
he images o he sequence.
I he poin
o
he es ima ed con ou is aken as he sea ch
cen e o each adii, he c i e ium
o
con ou sea ch is applied
o a window de ined a ound his de ined poin . The window
size mus be in oduced as inpu
o
he algo i hm. The window
size mus be la ge enough in o de o de ec he poin s ha
belong o he eal con ou pe each adii
,
bu small enough
in
such a way ha o he ca diac s uc u es do no a ec he con ou
de ec ion. Fo he gi en images, good esul s ha e been
ob ained using a window size o
10
pixels,
i.e.,
5
abo e and
5
below he es ima ed cen e .
The applied c i e ia ha e been: 1) De ec ion o he absolu e
maximum,
2)
Maximum o he absolu e de i a i e,
3)
Maximum o he posi i e de i a i e and
4)
Fi s non isola ed
maximum. The bes esul s in he ca i y con ou de ec ion ha e
been ob ained using he c i e ia o de ec ion o maximum
posi i e de i a i e.
In
he ex e nal con ou de ec ion (pe ica dium) he bes
esul s ha e been ob ained using he c i e ia o he maximum o
he absolu e alue o he de i a i e.
The c i e ia o he maximum absolu e alue in a es ic ed
egion p o ides a
goad
de ec ion in he lowe pa bu since he
image con ains a lo o noise, i p o ides a bad de ec ion in he
egion co esponding o he endoca dium, i.e., egion be ween
bo h con ou s. The de ec ion using he maximum absolu e
de i a i e p o ides
good
esul s in he i s s age o he
sea ching p ocess. The esul o his ii sea ch
is
alida ed by
de ec ing se s o poin s h ough some connec i i y c i e ium,
like o ins ance maximum dis ance be ween de ec ed pixels
belonging o he same se and minimum leng h o span
(minimum numbe o de ec ed consecu i e poin s e i ying he
connec i i y c i e ia in o de o conside ha se o poin s alid).
The c i e ia o
span
de ec ion se es o elimina e poin s ha ha e
been de ec ed in a posi ions o he eal con ou . The esul ing
poin s o his de ec ion ha do no belong o any span a e
elimina ed.
A
i s app oxima ed con ou is buil wi h he alida ed
poin s. This con ou is cons uc ed by inding he Disc e e
Fou ie T ans o m
(
DFT
)
o he alida ed poin s, and limi ing
he esul ing spec um o some speci ied equency samples.
Good esul s a e ob ained wi h
5
equency samples. I
he
in e se DCT is ound om hese samples, a smoo hed con ou
is ob ained ha app oxima es e y much he de ec ed poin s.
Since he de ec ion by de i a i e means o de ec he
ansi ions be ween e y di e en g ay le els, he esul o he
de ec ed smoo hed poin s always gi es a con ou ha in some
egions
o
he image is smalle han he eal con ou . This is
sol ed by doing a new sea ch in which he s a ing poin is
p ecisely his i s app oxima ed con ou ins ead o he ini ial
es ima ion ob ained by he lineal in e pola ion om he
s a
and
end images o he sequence. The sea ch c i e ium o his second
p ocess is he absolu e alue. This sea ch is no done a ound he
app oxima e con ou
-
as in he iis sea ch
-
bu s a ing om
he app oxima e posi ion o he con ou pe each adii and
mo ing owa ds he ou e posi ion. Good esul s a e ob ained
wi h sea ch windows o
4
pixels.
The inal con ou is ob ained building a smoo hed con ou
based
on
he DFT and in e se Dm wi h a limi ed numbe o
equency samples
o
he de ec ed poin s
in
his second sea ch.
The same me hod is applied
in
he de ec ion o he ca i y
in e nal con ou (endoca dium). The decision c i e ia a e
he
only signi ica i e changes. In he i s de ec ion he applied
c i e ium
is
ha
o
de ec ion by he maximum alue
o
he
posi i e de i a i e. This a oids he s ong ansi ions o g ay
le els
in
egions e y close o he con ou posi ion caused by
papilla y muscles o al es,
in
sec ions co esponding o
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de e mined egions o he le en icle in pa as emal sho axis
iews.
Once he decision c i e ium has been applied, he algo i hm
is exac ly he same
as
ha o he ex e nal con ou .
Fig.
3
shows he
inal
ex emal and in e nal con ou s o
a
empo al sequence o 10 images be ween end sys ole and end
dias ole images.
1V.CONCLUSIONS
A ully au oma ed algo i hm ha ex ac s en icula
endoca dial and epica dial bo de s o empo al pa as emal sho
axis iews has been de eloped. The algo i hm may be applied
o o he echoca diog aphic iews wi h he same amoun
o
success. Once he ex e nal and in e nal con ou s a e ound,
en icula chambe olume and o he ela ed pa ame e s can be
ound.
REFERENCES
[31
[41
S.Collins, D.J.Sko on "Ca diac Imaging and Image
P ocessing" Mc.G aw-Hill, 1986
D.J.
Sko on, e
al.
"Digi al signal and image p ocessing in
echoca diog aphy" Ame ican Hea jou nal Vol.
110,
NQ
6.
pp.1266-1283, Dec. 1985
L.
Zhang, E.A. Geise "An e ec i e algo i hm o
ex ac ing se ial endoca dial bo de s om wo-dimensional
echoca diog ams" IEEE T ans. Bio. Eng. BME-31, pp.
L.To es e
al.
"Classical me hods o le enmcula con ou
ex ac ion and p ep ocessing o echoca diog aphic images
:
A
e iew." Ul asonics In ema ional 89, Mad id, 3-7, July
441-447, 1984
1989
[SI
A.Gasull e al. "Au oma ic le en icula con ou ex ac ion
o olume calcula ion om echoca diog aphic images"
Ul asonics In ema ional89, Mad id,
3-7
July 1989
Fig.3. Ex e nal and In e nal con ou s o an end sys ole-end
dias ole ca diac cycle
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