Resea ch A icle
Au oma ic CDR Es ima ion o Ea ly Glaucoma Diagnosis
M. A. Fe nandez-G ane o,
1
A. Sa mien o,
2
D. Sanchez-Mo illo,
1
S. Jiménez,
3
P. Alemany,
3
and I. Fondón
2
1
Biomedical Enginee ing and Telemedicine Resea ch G oup, Uni e si y o Cádiz, Pue o Real, Cádiz, Spain
2
Signal Theo y and Communica ion Depa men , Uni e si y o Se ille, Se ille, Spain
3
Oph halmology Uni , Pue a del Ma Hospi al, Cádiz, Spain
Co espondence should be add essed o M. A. Fe nandez-G ane o; [email p o ec ed] and I. Fondón; [email p o ec ed]
Recei ed 1 June 2017; Re ised 9 Sep embe 2017; Accep ed 24 Sep embe 2017; Published 27 No embe 2017
Academic Edi o : And eas Maie
Copy igh © 2017 M. A. Fe nandez-G ane o e al. This is an open access a icle dis ibu ed unde he C ea i e Commons
A ibu ion License, which pe mi s un es ic ed use, dis ibu ion, and ep oduc ion in any medium, p o ided he o iginal wo k
is p ope ly ci ed.
Glaucoma is a degene a i e disease ha cons i u es he second cause o blindness in de eloped coun ies. Al hough i canno be
cu ed, i s p og ession can be p e en ed h ough ea ly diagnosis. In his pape , we p opose a new algo i hm o au oma ic
glaucoma diagnosis based on e inal colou images. We ocus on cap u ing he inhe en colou changes o op ic disc (OD) and
cup bo de s by compu ing se e al colou de i a i es in CIE L
∗
a
∗
b
∗
colou space wi h CIE94 colou dis ance. In addi ion, we
conside spa ial in o ma ion e aining hese colou de i a i es and he o iginal CIE L
∗
a
∗
b
∗
alues o he pixel and adding o he
cha ac e is ics such as i s dis ance o he OD cen e. The p oposed s a egy is obus due o a simple s uc u e ha does no
need nei he ini ial segmen a ion no emo al o he ascula ee o de ec ion o essel bends. The me hod has been ex ensi ely
alida ed wi h wo da ase s (one public and one p i a e), each one comp ising 60 images o high a iabili y o appea ances.
Achie ed class-wise-a e aged accu acy o 95.02% and 81.19% demons a es ha his au oma ed app oach could suppo
physicians in he diagnosis o glaucoma in i s ea ly s age, and he e o e, i could be seen as an oppo uni y o de eloping
low-cos solu ions o mass sc eening p og ams.
1. In oduc ion
The Wo ld Heal h O ganiza ion (WHO) has epo ed an
inc ease o he numbe o pa ien s suffe ing om eye dis-
eases due o he aging o wo ld popula ion [1]. Among all
o hem, glaucoma is he second leading cause o blindness
in de eloped coun ies. This disease is conside ed as a
majo public heal h conce n, and i s p e alence will
p obably con inue o inc ease as li e expec ancy con inues
o ise [2].
Glaucoma desc ibes a g oup o ocula diso de s wi h a
common cha ac e is ic: he p og essi e loss o ne e fibe s
in he e ina. Al hough i canno be cu ed, i s associa ed
blindness may be p e en ed h ough ea ly diagnosis. How-
e e , glaucoma is known as he “silen he o sigh ”in he
sense ha i p esen s no symp oms un il ision is al eady los .
Glaucoma should be diagnosed ea ly in he disease cou se in
o de o iden i y pa ien s ha equi e ea men o main ain
quali y o li e [2].
The loss o op ical fibe s due o glaucoma p og ession is
associa ed wi h a co esponding change in he op ic disc
(OD). The e o e, he emp y space wi hin he OD and he
so-called cup is subsequen ly enla ged. Tha is he eason
why he cup o disc a io (CDR), defined as he ela ion
be ween he OD and cup a ea, inc eases wi h he p og ession
o he disease. OD appea ance is, he e o e, c i ical in glau-
coma diagnosis, and images o he e ina a e manda o y o
a co ec disease assessmen .
Se e al eye imaging echnologies ha e been de eloped
du ing he las 160 yea s [3]. Heidelbe g e ina omog aph
(HRT) and op ical cohe ence omog aphy (OCT) along wi h
angiog aphy a e widely used in he diagnosis and ollow-up
o pa ien s wi h diffe en ocula diseases such as diabe ic
e inopa hy o macula degene a ion [3, 4]. Al hough OCT
Hindawi
Jou nal o Heal hca e Enginee ing
Volume 2017, A icle ID 5953621, 14 pages
h ps://doi.o g/10.1155/2017/5953621
p o ides he bes ep esen a ion o he e ina, de ices based
on his echnique a e highly expensi e and hey canno be
affo ded by local medical cen es [5]. As undus imaging is
he mos es ablished way o e inal imaging in p ima y ca e
se ings, an au oma ic glaucoma diagnosis sys em based on
undus images could be deployed ha ing he po en ial o
ea ly disease diagnosis [6].
Ne e heless, he use o undus imaging echniques alone
could no be enough o a mass sc eening p og ams. The lack
o specialis s in local heal h cen es makes he inspec ion o
e e y pa ien ’s e inal image unaffo dable. Mo eo e , he
amoun o in o ma ion would exceed he limi o clinicians’
abili y o ully u ilize i [3]. Unde hese ci cums ances, he
use o au oma ed imaging classifica ion as a iage es may
p o e o be cos effec i e [7].
Image-based glaucoma diagnosis is pe o med mainly
wi h CDR measu emen , ha is, he compu a ion o he
a io o OD and cup egion a eas. Cu en ly, his calculus
is pe o med on he basis o manually delinea ed a eas
o e he e inal undus image. The skilled human g ade
mus ca e ully d aw he egion wi h an image edi o so -
wa e, and a e wa ds, he a io o he a eas is calcula ed.
This me hod is ime consuming and exhaus ing. A li le
sa ing in ime is p o ided by some acquisi ion o de ices
ha offe he possibili y o ex ac ing he OD and cup
egion by adjus ing an ellipse o ou poin s ha should
be in oduced by he expe . Ins ead o ca e ully ma king
he whole egion, he physician should only ma k ou
e e ence poin s. Howe e , assuming ha he a ea is ellip-
ical and basing he adjus men on ou poin s makes he
sys em a li le bi as e , ha is, abou eigh minu es pe
eye unde he Klein p o ocol [8], bu less accu a e. I
seems clea ha he medical communi y needs an au o-
ma ic me hod o CDR compu a ion. A compu e -aided
diagnosis ool (CAD) in eg a ing such an algo i hm could
a oid p oblems o inaccu a e esul s while sa ing ime
and cos s.
Fo au oma ic CDR es ima ion, he OD and cup egions
ha e o be segmen ed based on hei cha ac e is ic appea -
ance (Figu e 1). Howe e , i mus be no iced ha he shape,
size, and colou a ia ions on e inal images ac oss a popula-
ion a e expec ed o be high [3], making OD and cup segmen-
a ion a challenging ask (Figu e 2). Gene ally speaking, OD
is an ex emely in ense egion inside he undus image and
can be iden ified om ea u es such as he ollowing [9]:
(i) Shape: he OD is oughly ci cula .
(ii) Colou : he OD usually p esen s hues anging om
o ange o yellow.
(iii) B igh ness: he OD p esen s a b igh ness alue ha
is usually highe han he es o he e inal image.
(i ) Size: he OD a ea is usually less han 1/7 o he o al
eye.
The op ic cup is imme sed wi hin he OD egion. I usu-
ally p esen s a oughly ci cula shape and a b igh yellowish
colou as can be app ecia ed in Figu e 1. Howe e , i is well
known ha i s segmen a ion om e inal undus images is
a duous, due o he lack o dep h in o ma ion, which is
no a ailable in he 2D images. Fu he mo e, he p esence
o ill-defined and inhomogeneous op ic cup bounda ies
(see Figu e 2) makes he p oblem e en mo e difficul [10].
F om he abo emen ioned cha ac e is ics o he OD and
cup, colou is he mos ele an when ying o isola e bo h
a eas [4]. Consequen ly, he p ope colou space selec ion is
c ucial o he e en ual success o he algo i hm. Con e sely,
i is a gene al end o only conside he illumina ion in o -
ma ion o pixels [11–21]. Two ac s a e p esen ed by he
majo i y o pape s o suppo hei selec ion:
(1) The use o colou images in ol es highe complexi y
due o hei h ee-dimensional na u e.
(2) G ey le el images allow using well-known algo-
i hms [22].
Some o he me hods using g ey scale images selec only
one colou plane o he h ee a ailable in any colou image
ep esen a ion (RGB, HIS, e c.). Mos o hese a icles claim
ha he OD can be easily disc imina ed om he G channel
when analysing he RGB componen s o he image [9, 23–33].
F equen ly, blood essels need o be p e iously inpain ed
o p e en an in e e ence wi h he OD segmen a ion
algo i hm [23, 27, 33]. Likewise, he e is a common end
o using basic image p ocessing echniques such as his o-
g am h esholding alone [30] o combined wi h o he
me hods [23–25, 31–34].
Equally impo an is he use o he R colou plane
[35–38], he V channel om HSV, [5, 39, 40], o he M
colou channel o CMY [41]. Only a mino i y o me hods
elies on he luminance coo dina e (L
∗
) o CIE L
∗
a
∗
b
∗
colou
space [42].
Se e al au ho s p e e he use o mo e han wo colou
planes usually om RGB colou space. The p ocessing is
pe o med sepa a ely, as i diffe en g ey le el images we e
a ailable [8, 10, 43, 44].
Op ic
disc
Cup
Figu e 1: The OD and cup as seen on a ypical e inal undus image.
The OD is p esen ed as an almos ci cula egion wi h a colou
anging om o ange o yellow. The cup is he b igh es egion
wi hin i , wi h a diffuse bo de only dis inguishable by essel bends.
2 Jou nal o Heal hca e Enginee ing
The abo emen ioned echniques a e based on g ey le el
image p ocessing. F om a compu a ional poin o iew, he
use o scala alues may educe p ocessing ime. Ne e heless,
he co ela ion among diffe en colou planes is neglec ed by
hese app oaches, and hence, some use ul ea u es may be
los . Only ac oss he in eg a ion o he in o ma ion in all
channels, a colou image can be effec i ely segmen ed.
The use o he ull colou ep esen a ion on au oma ic
diagnosis assessmen is impo an . Howe e , equally sig-
nifican is he ole o colou pe cep ion in objec ecog-
ni ion and scene unde s anding bo h o humans and
in elligen ision sys ems [45, 46]. Among all he possibil-
i ies o colou image ep esen a ion, he use o a uni o m
colou space, ha is, a ep esen a ion o he image whe e
colou dis ances a e co ela ed o pe cep i e diffe ences
could benefi he quali y o he esul s. Some au ho s ha e
used CIE L
∗
a
∗
b
∗
colou space due o i s uni o mi y and
he possibili y o using ad ance colou me ics [47–49].
Au ho s in [50] used JCh colou space om he CIE-
CAM02 colou appea ance model o OD ex ac ion.
Al hough hese me hods p e end o ake ad an age o he
comple e colou in o ma ion a ailable while being as close
o human pe cep ion as possible, OD segmen a ion is a
p oblem ye o be sol ed. Fo ins ance, au ho s in [50]
p ocess only he g ey le el plane J. Me hods illus a ed in
[34] and [48] would no wo k in images whe e colou
diffe ences be ween OD and backg ound a e no signifi-
can . The compu a ion o colou de i a i es, only in ce -
ain pixels loca ed in a adius cen ed on he OD, was
p esen ed in [47]. In such app oach, he final ob ained
bo de is dependen on he sepa a ion o he adial lines.
Rega ding cup segmen a ion, he same limi a ions abou
colou spaces and human pe cep ion could be applied. I is
impo an o no e ha he cup a ea is mo e difficul o seg-
men han OD due o essels, bo de asymme y, and colou
a iabili y. I usually happens ha images p esen no b igh
yellowish a ea a all bu he cup is s ill he e. In hese cases,
he cup edge is dic a ed by essel bends. Fo his eason, he
majo i y o app oaches p esen ed in he pas may no gi e
accu a e esul s when dealing wi h complex image da abases
[5, 8, 10, 26–28, 30–34, 40, 41, 44, 49, 51–54].
Al hough he abo emen ioned echniques p esen ele-
an esul s, he e a e s ill some weak poin s ha should
be add essed:
(1) The use o colou in o ma ion is usually limi ed o
sepa a ely p ocessing each colou plane. Howe e ,
e inal undus images a e ec o - alued colou
images and he e o e, hei analysis in a scala ash-
ion could add some e o s o he p ocess.
(2) Medical image pe cep ion is no add essed by he
majo i y o he app oaches. The use o uni o m col-
ou spaces o ad anced colou dis ances is limi ed.
(3) The complexi y o he p oposed echniques is high
making he ools unconnec ed and he me hod
inelegan .
(4) The p oposed me hods ely on essel de ec ion and
inpain ing equen ly. In many app oaches, essel
bends mus be compu ed as well. The e o s in his
ini ial s age will p opaga e o he es o he algo i hm.
(5) The me hods a e designed and es ed in he same
image da abases, wi h a limi ed numbe o images.
These da abases a e p i a e in mos cases. The gold
s anda d is usually no a ailable. The e o e, he eal
quali y o he ool canno be accessed.
To add ess hese issues, he p esen echnique has he
ollowing key poin s:
(1) The me hod is simple. I has h ee s ages only.
(2) I does no ely on he segmen a ion, inpain ing, o
de ec ion o essel bends o o he e inal image
s uc u es.
(3) The me hod makes use o a uni o m colou model
along wi h a colou pe cep ion-adap ed dis ance
image.
(4) The echnique has been ex ensi ely alida ed. I has
been designed on public image da abases. The esul
(a) (b) (c) (d)
Figu e 2: The OD p esen s a gene al appea ance making i sui able o i s au oma ic de ec ion. Howe e , he e is a high a iabili y among
popula ion: (a) clea colou change and ed hue, well-defined bo de ; (b) sub le colou change and ed hue, diffuse bo de ; (c) sub le
colou change and pale yellow uzzy bo de ; and (d) b igh yellow diffuse bo de p esence o pe ipapilla y a ophy.
3Jou nal o Heal hca e Enginee ing
o he es on hese da abases is p esen ed. Once he
ool has been ained, a second expe imen is pe -
o med using a comple ely diffe en da abase.
(5) As glaucoma diagnosis on e inal undus images is
cu en ly pe o med mainly by manual inspec ion,
eehand, o ellipse fi ed, we do no p esen only
he segmen a ions o hese a eas bu also he CDR
measu emen s ha a e au oma ically calcula ed. We
compa e he esul s o he echnique wi h he gold
s anda d p o ided by expe s wi h bo h o he
app oxima ion me hods gene ally used.
2. Ma e ials and Me hods
2.1. Image Da abase. We cons uc ed wo image da abases,
namely, Da ase 1 and Da ase 2, each con aining 60 e inal
undus images. These 120 images spanned a g ea di e si y
o e inal con en . The key poin on selec ing he images
was ha hey needed o be ep esen a i e o he con en ha
he algo i hm will encoun e on i s p ac ical use.
The e o e, we explo ed se en publicly a ailable da abases
[55–61] o c ea e Da ase 1. Six y images ha offe a wide
ange o appea ances, illumina ion, and colou s we e selec ed
as shown in Figu e 3 (a de ailed lis o images can be accessed
in he supplemen a y ma e ial a ailable online a h ps://doi.
o g/10.1155/2017/5953621). The image da abase comp ised
heal hy and unheal hy images o pa ien s suffe ing om
glaucoma in some cases and also diabe ic e inopa hy. Two
expe s pe o med manual anno a ion o all o he e inog a-
phies since public gold s anda d was no a ailable.
Da ase 2 included 60 images om he Su ge y Depa -
men and Glaucoma Uni o he Uni e si y Hospi al Pue a
del Ma o Cadiz (Spain). Images we e anno a ed by wo
expe s and we e used as an independen es se . The
complexi y o he images o Da ase 2 was high, as can be
app ecia ed in Figu e 4, including challenging cases wi h no
isible cup, p esence o abno mali ies, o diffuse bo de s. In
Da ase 2, wo gold s anda ds we e used:
(a) The fi s gold s anda d consis ed o he eehand
d awing on he e inal image. I was a edious and
ime-consuming ask due o he difficul y o selec ing
he p ecise bo de o he OD and cup egions.
(b) As a second gold s anda d, he OCT so wa e pe -
o med ellipse fi ing. Expe s ma ked up ou poin s
o bo h egions. I mus no be con used wi h image
p ocessing-based ellipse fi ing. The OCT so wa e
only compu es he equa ion o an ellipse based on
he ou manually ma ked poin s. No image in o ma-
ion is aking in o accoun .
Once he da abases we e buil , a egion o in e es (ROI)
was au oma ically selec ed in o de o educe compu a ional
(a) (b) (c) (d)
Figu e 3: Da ase 1 comp ises a wide ange o OD and cup appea ances due o hei diffe en na u e, popula ion, and acquisi ion de ices.
(a) (b) (c) (d)
Figu e 4: Da ase 2 is a p i a e da abase compounded by e inal undus images acqui ed by he same de ice. The o e all complexi y is high
due o he p esence o many diffe en appea ances: uzzy edges, sub le colou changes, a ophies, and so o h.
4 Jou nal o Heal hca e Enginee ing
ime [11, 17, 18, 21, 27]. The ROI a ea co esponded o a
egion wi h he ollowing cha ac e is ics:
(i) Squa e shape
(ii) Cen ed on he OD
(iii) Wi h an a ea equal o 1/7 o he e ina size
I mus be no iced ha any o he me hods p esen ed
in li e a u e abou OD loca ion could be used in his s ep
[9–47]. Howe e , he con ibu ion o he p oposed ech-
nique ela es on OD and cup de ec ion and no OD
localiza ion. In o de o effec i ely e alua e he pe o -
mance o he echnique no dis u bing i wi h possible e o
p opaga ion, we ha e manually inpu OD cen es o all o
he images.
2.2. Vec o -Based Colou De i a i es. Image de i a i es
we e used in o de o iden i y OD and cup bounda ies,
due o hei capabili y o cap u ing changes on a ce ain
pixel neighbou hood. De i a i es can be compu ed in se -
e al di ec ions by o a ing he ke nel be o e pe o ming
he con olu ion.
Re inog aphies a e colou images. Consequen ly, he
edges should be ound by looking o colou changes. Edge
de ec ion in colou images is usually pe o med by applying
he de i a i e ke nels o he h ee colou channels indepen-
den ly and hen by combining he esul s. These kinds o
me hods do no ake in o accoun he co ela ion among
colou channels, and, he e o e, hey end o miss edges ha
ha e he same s eng h bu in opposi e di ec ions in wo o
hei colou componen s [62]. In an a emp o a oid his
issue, we ha e adop ed he echnique p oposed in [62], whe e
colou images a e ea ed as wo dimensional (pixel loca ion),
h ee-channel (colou planes) ec o fields. Then, hey can be
cha ac e ized by a disc e e in ege unc ion I(x,y) ha can be
w i en as ollows:
Ix,y=CP
1x,y,CP
2x,y,CP
3x,y, 1
whe e CP1x,y,CP
2x,y, and CP3x,yco espond o
colou channels and x,y o pixels’loca ions. Fo ins ance,
in RGB colou space
CP1x,y=Rx,y,2
CP2x,y=Gx,y, 3
CP3x,y=Bx,y4
The magni ude o maximum a ia ion a pixel x,ywi h
an o ien a ion o 0
°
is defined as ollows [62]:
Bx,y=ΔVx,y, 5
whe e ΔV,i Euclidean dis ance (ΔE) is used, is defined
as ollows:
ΔVx,y=ΔEV
+x,y,V−x,y6
The quan i ies V+,V−,H+,and H−a e he con olu ion
ke nels whose ou pu s a e ec o s co esponding o he local
a e age colou s. Le he edge masks (k)be
−
1 −
2 −
3
000
+
1 +
2 +
3
, 7
and he image neighbou hood (W)
w1w2w3
w4w5w6
w7w8w9
8
Each wi,i=1,…,9, is a ec o wi h h ee componen s
co esponding o each colou plane. Then,
V+= +
1w7+ +
2w8+ +
3w9, 9
V−= −
1w1+ −
2w2+ −
3w310
To imp o e adap a ion o human pe cep ion o he
me hod, ins ead o using Euclidean dis ance o mula as in
(3), he echnique p esen ed in [63] was ollowed. The colou
space CIE L
∗
a
∗
b
∗
was selec ed due o i s uni o mi y. Fo he
colou dis ance o mula, CIE94 was adop ed ins ead o
Euclidean, due o i s bes pe o mance and lowe compu a-
ional ime when compa ed o o he pe cep ion-adap ed
colou diffe ences such as CIEDE2000. Then,
wi=wL∗
i,wa∗
i,wb∗
i, i=1,…,9 11
Equa ion (9) can be ew i en as
V+= +
1wL∗
7+ +
2wL∗
8+ +
3wL∗
9,
+
1wa∗
7+ +
2wa∗
8+ +
3wa∗
9,
+
1wb∗
7+ +
2wb∗
8+ +
3wb∗
9
=VL∗
+,Va∗
+,Vb∗
+,
12
ha is, each local a e age colou will ha e i s h ee-colou
componen s.
Following he same p ocedu e, (10) can be ew i en as
ollows:
V−=VL∗
−,Va∗
−,Vb∗
−13
Subsequen ly,
ΔV=ΔE94 V+,V−, 14
whe e ΔE94 is he CIE94 colou dis ance be ween he co e-
sponding ec o s:
5Jou nal o Heal hca e Enginee ing

ΔE94 =ΔL∗
kLSL
2
+ΔC∗
ab
kCSC
2
+ΔH∗
ab
kHSH
2
15
Fo he case o ΔV,
ΔL∗=VL∗
+−VL∗
−,
Δa∗=Va∗
+−Va∗
−,
Δb∗=Vb∗
+−Vb∗
−,
C∗
+=Va∗
+
2+Vb∗
+
2,
C∗
−=Va∗
−
2+Vb∗
−
2,
ΔC∗
ab =C∗
+−C∗
−,
ΔH∗
ab =Δa∗2+Δb∗2+ΔC∗
ab
2,
SL=1,
SC=1+00045C∗
+,
SH=1+00015C∗
−,
kL=kC=kH=1
16
The magni ude o maximum a ia ion can be compu ed
using (4). In he case ha we wan o calcula e colou changes
in o he di ec ions a he han e ical (0
°
), we only need o
o a e he mask on (7) o he desi ed o ien a ion.
As s a ed on he in oduc ion, he OD and cup egions
p esen cha ac e is ic appea ances di ec ly ela ed o colou .
Howe e , he absolu e colou alue o a pixel should no be
us ed. Figu es 2–5 show how colou a iabili y is oo high
o de e mine a specific colou ange o e e y OD and cup
a ea. On he con a y, e e y OD and cup bo de p esen a
change o colou when compa ed o hei su ounding pixels.
In o he wo ds, absolu e colou alues a e no disc imina i e
bu hei ela i e changes on he e ina can be (Figu e 6).
In he p esen app oach, we ha e aken ad an age o his
ela i e change o colou o de ec pixels belonging o OD
and cup edges. We compu ed Sobel ec o -based colou
de i a i es in 25 o ien a ions ( om 0
°
o 360
°
wi h a sepa a-
ion in e al o 15
°
) o e e y pixel wi hin he image. To
implemen he Sobel ope a o , he mask o (10) a 0
°
was
−1−2−1
000
121
, 17
(a) (b) (c) (d)
Figu e 5: Cup egion segmen a ion is a challenging ask due o i s wide ange o appea ances: (a) b igh yellow, well-defined bo de and small
size, (b) no pe cep ible colou change, (c) pale yellow, well-defined bo de and medium size, and (d) pale yellow, diffuse bo de and la ge size.
Figu e 6: Colou changes ep esen ed by g adien a ows ma ked in blue offe he necessa y in o ma ion o OD and cup segmen a ion.
6 Jou nal o Heal hca e Enginee ing
while o 45
°
, he mask was
−2−20
−101
012
18
Equa ion (2) was e alua ed o measu e he maximum
colou a ia ion o e e y pixel and o ien a ion. Figu e 7
shows some examples whe e each pixel alue co esponds
o i s B alue on ha di ec ion.
2.3. Classifica ion Based on Bagged T ees. OD de ec ion and
cup de ec ion we e achie ed by classi ying each pixel on he
image ega ding i s ec o -based colou de i a i es and i s
dis ance o he OD cen e.
Se e al classifie s we e used. The bes pe o mance was
ob ained wi h a bagged ees classifie , as i will be explained
in Resul s. The e o e, he bagged ees classifie s will be
desc ibed b iefly in his sec ion.
The idea o bagging is o ob ain he bes model by com-
bining he esul s o mul iple weak classifie s in o a single
and s ong one [64]. In a bagged ee, he basic classifie is
a decision ee.
Da a is di ided in o T aining se s o size n, and T
decision ees a e ained wi h hose se s, each one ying o
fi he model. The Tdecisions a e finally combined wi h a
majo i y o ing ule. Bagging leads o imp o emen s o
uns able p ocedu es [65].
3. Resul s and Discussion
The disc imina i e powe o colou de i a i es when he OD
and cup a e de ec ed has been es ed. To ha pu pose, a ea-
u e ec o mus be buil o e e y pixel on each ROI. This
ea u e ec o is he inpu o he classifie ha will assign a
p obabili y alue o ha pixel ega ding i s sui abili y o
belonging o he OD, cup, o backg ound. The ea u e ec o
con ains all o he colou a ia ion alues o he 25 o ien a-
ions, o iginal CIE L
∗
a
∗
b
∗
alues o he pixel, i s dis ance, and
he angle ega ding he cen e o he OD and i s posi ion.
These 32 ea u es combine he a p io i colou and spa ial
knowledge abou he OD and he cup.
The me hod has been ex ensi ely alida ed and es ed
wi h wo expe imen s ca ied ou on bo h o he da abases
de ailed in Sec ion 2.
0°45°270°
Figu e 7: Vec o -based colou de i a i es o h ee o he compu ed o ien a ions: 0
°
,45
°
, and 270
°
.
7Jou nal o Heal hca e Enginee ing
3.1. Image Da abase Da ase 1. This da abase is composed o
60 e inal undus images om six diffe en public da abases.
Da ase 1 images p esen a a ie y o appea ances, illumina-
ion condi ions, e inal s uc u es, and so o h. Conse-
quen ly, i is expec ed ha an algo i hm de eloped using
his da abase will be highly obus . A o al o six classifie s
we e ained and alida ed:
(i) Simple ee (ST).
(ii) Bagged ee (BT). This classifie was in oduced in
Sec ion 2.3.
(iii) Complex ee (CT). This is a decision ee wi h many
lea es ha makes many fine dis inc ions be ween
classes.
(i ) Linea disc iminan (LD).Decisions a e made by
es ima ing, wi h Bayes heo em, he p obabili y ha
a new se o samples belongs o each class.
( ) Quad a ic disc iminan (QD).This classifie is an
ex ension o LD whe e he e ogeneous a iance-
co a iance ma ices a e conside ed.
( i) kNN Euclidean. kNN does no use a model o fi
he aining da a and subsequen ly classi y he new
samples [66].
Model selec ion was pe o med using c oss- alida ion.
Fo each classifie , he accu acy o he bes pa ame e se ing
was compa ed. Da ase 1 was used as a old c oss- alida ion
se , which was subsequen ly and epea edly di ided in o ain
and alida ion se s. Fo his in e nal alida ion, 10- old c oss-
alida ion was pe o med. In each o he en olds, he
classifie was ebuil om sc a ch. This en i e p ocedu e is
epea ed 10 imes. The epo ed accu acy was he a e age
o e he accu acies o each old.
The abili y o he algo i hm o pe o m an accu a e clas-
sifica ion o OD, cup, and backg ound pixels was measu ed
by i s sensi i i y while i s abili y o de e mine he pixels ha
do no belong o each o he h ee classes was exp essed by
i s specifici y. Posi i e p edic i e alue (PPV) and nega i e
p edic i e alue (NPV) we e also compu ed o gi e an idea
o he p opo ions o ue posi i es and ue nega i es.
Table 1 shows he pe o mance me ics ha we e used in his
s udy o e alua e he use o ec o -based colou de i a i es in
combina ion wi h each o he six classifie s.
The analysis o Table 1 e eals ha BT classifie , which
showed a class-wise-a e aged accu acy o 95.02%, p o ides
he bes combina ion o classifie and ec o -based colou
de i a i es. This classifie showed a specifici y o 99.23% o
he OD class and 99.80% o he cup class, while p ese ing
a sensi i i y o 91.75% o he OD and 90.63% o he cup.
PPV alues we e 90.74% o he OD class and 94.83% o
he cup class. NPV was 99.32% and 99.62% o he OD and
cup classes, espec i ely.
3.2. Image Da abase Da ase 2. This image da abase com-
p ised 60 images p o ided by he Uni e si y Hospi al Pue a
del Ma , Cadiz, Spain. Images we e acqui ed in a ou ine
sc eening p ocess wi h he same acquisi ion de ice.
Ex e nal alida ion es ablishes models’ anspo abili y
and gene alizabili y [67]. In his s udy, independen Da ase
2 was used o ex e nally alida e he ully ained classifie
p e iously selec ed using c oss- alida ion in Da ase 1. All
samples in Da ase 1 we e used o ain he BT model. Two
expe s made wo anno a ions o each o he images on
he da abase:
(i) F eehand: Two glaucoma specialis s me iculously
anno a ed he exac bo de o he OD and he cup.
This p ocess was ime consuming al hough cons i u -
ing he mos exac e e ence o e o calcula ion.
This anno a ion was conside ed he gold s anda d
o ou expe imen s.
(ii) Ellipse based: The OD and cup edges we e ob ained
by building an ellipse ha con ained ou poin s
Table 1: Model pe o mance e alua ion unde Da ase 1 da abase. The h ee classes a e backg ound (class 1), op ic disc (class 2), and cup
(class 3). (a) Simple ee, (b) bagged ees, (c) KNN, (d) complex ee, (e) linea disc iminan , and ( ) quad a ic disc iminan .
Indica o a (%) b (%) c (%) d (%) e (%) (%)
Accu acy 95.54 98.66 97.22 96.63 85.55 89.11
Class-wise-a e aged accu acy 82.70 95.02 89.39 86.99 53.28 61.39
Sensi i i y class 1 98.17 99.61 99.31 98.88 90.59 95.19
Sensi i i y class 2 74.35 91.75 82.23 79.16 46.84 40.70
Sensi i i y class 3 77.21 90.63 78.94 79.55 46.42 45.16
Specifici y class 1 89.43 96.20 91.24 90.90 74.78 63.71
Specifici y class 2 97.29 99.23 98.61 98.07 91.73 94.56
Specifici y class 3 99.23 99.80 99.50 99.43 95.93 98.23
PPV class 1 98.62 99.51 98.87 98.82 96.52 95.29
PPV class 2 69.30 90.74 82.91 77.12 31.77 38.07
PPV class 3 80.17 94.83 86.40 85.04 31.55 50.81
NPV class 1 86.35 96.93 94.51 91.30 50.76 63.20
NPV class 2 97.88 99.32 98.54 98.28 95.45 95.10
NPV class 3 99.08 99.62 99.15 99.18 97.79 97.79
8 Jou nal o Heal hca e Enginee ing
manually ma ked by he expe s. This s a egy
showed a lowe compu a ional ime. Howe e , he
ob ained bo de was no as accu a e as ha in he
eehand app oach.
3.2.1. Quali y o he Segmen a ion. Afi s expe imen was
pe o med o effec i ely know whe he a pos p ocessing s ep
would e en ually imp o e he quali y o he esul s. To his
pu pose, we ook he p obabili ies o each pixel o belonging
o each class and, om his in o ma ion, we buil wo p oba-
bili y images (see Figu e 8) ha a e he basis o he final
pos p ocessing. This las s ep was pe o med in wo ways:
(i) Ac i e con ou (AC) based: The p obabili y images
ha co esponded o he OD and he cup we e h e-
sholded o ob ain an ini ial mask. The h eshold was
au oma ically ob ained wi h O su’s echnique. The
final OD and cup we e segmen ed on he base o his
bina y image and e ol ing on he co esponding
p obabili y alues. The Chan-Vese model [68] wi h
a numbe o i e a ions expe imen ally fixed o 20
was used o AC.
(ii) AC and ellipse fi ing: This pos p ocessing consis ed
on au oma ically adap ing an ellipse o he bounda y
pixels ob ained wi h he p e ious pos p ocessing.
These wo pos p ocessing s eps we e added o emula e
expe s’segmen a ions, which we e in gene al smoo he
han ou algo i hm’s esul s (Figu e 9(a)). AC p o ides he
so ness equi ed while p ese ing he shapes (Figu e 9(b)).
The ellipse-fi ed esul was in ended o be e compa e
wi h manually ma ked ellipses p o ided wi h he da abase
(Figu e 9(c)).
(a) (b) (c)
Figu e 8: (a) O iginal ROI image, (b) OD p obabili y image, and (c) cup p obabili y image.
(a) (b) (c)
Figu e 9: Resul images o he o iginal ROI o Figu e 8. Whi e colou co esponds o he cup, g ey colou o he OD, and black colou o he
backg ound. Labels assigned by he classifie : (a) wi hou pos p ocessing, (b) wi h AC, and (c) wi h AC and ellipse fi ing.
Table 2: Bagged ee model pe o mance e alua ion unde
Da ase 2 da abase. The h ee classes a e backg ound (class 1),
op ic disc (class 2), and cup (class 3). (a) Wi hou pos p ocessing,
(b) smoo hed wi h AC, and (c) smoo hed and ellipse fi ing.
Indica o a (%) b (%) c (%)
Accu acy 94.54 94.75 94.61
Class-wise-a e aged accu acy 79.66 81.95 81.19
Sensibili y class 1 97.01 96.63 96.34
Sensibili y class 2 73.07 83.02 83.18
Sensibili y class 3 81.45 76.20 78.75
Specifici y class 1 90.30 93.61 94.47
Specifici y class 2 96.36 95.73 95.56
Specifici y class 3 99.01 99.43 99.33
Posi i e p edic i e alue class 1 98.68 99.13 99.24
Posi i e p edic i e alue class 2 62.37 61.61 60.75
Posi i e p edic i e alue class 3 77.93 85.10 83.58
Nega i e p edic i e alue class 1 80.11 78.74 77.47
Nega i e p edic i e alue class 2 97.74 98.55 98.57
Nega i e p edic i e alue class 3 99.20 98.98 99.09
9Jou nal o Heal hca e Enginee ing