Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images

12 Pages·2017·2.78 MB·English

applied
sciences
Article
Feature Selection and Classification of Ulcerated
Lesions Using Statistical Analysis for WCE Images
ShipraSuman1,*,FawnizuAzmadiHussin1,AamirSaeedMalik1,ShiawHooiHo2,IdaHilmi2,
AlexHwong-RueyLeow2andKhean-LeeGoh2
1 CenterforIntelligentSignal&ImagingResearch,UniversitiTeknologiPETRONAS,SeriIskandar32610,
Malaysia;[email protected](F.A.H.);[email protected](A.S.M.)
2 DepartmentofMedicine,UniversityofMalayaMedicalCenter,KualaLumpur50603,Malaysia;
[email protected](S.H.H.);[email protected](I.H.);[email protected](A.H.-R.L.);
[email protected](K.-L.G.)
* Correspondence:[email protected];Tel.:[phone]
Received:29July2017;Accepted:18September2017;Published:24October2017
Abstract: Wireless capsule endoscopy (WCE) is a technology developed to inspect the whole
gastrointestinal tract (especially the small bowel area that is unreachable using the traditional
endoscopyprocedure)forvariousabnormalitiesinanon-invasivemanner. However,visualization
ofamassivenumberofimagesisaverytime-consumingandtedioustaskforphysicians(proneto
humanerror). Thus,anautomaticschemeforlesiondetectioninWCEvideosisapotentialsolution
toalleviatethisproblem. Inthiswork,anovelstatisticalapproachwaschosenfordifferentiating
ulcer and non-ulcer pixels using various color spaces (or more specifically using relevant color
bands). ThechosenfeaturevectorwasusedtocomputetheperformancemetricsusingSVMwith
gridsearchmethodformaximumefficiency. Theexperimentalresultsandanalysisshowedthatthe
proposedalgorithmwasrobustindetectingulcers. Theperformanceintermsofaccuracy,sensitivity,
andspecificityare97.89%,96.22%,and95.09%,respectively,whichispromising.
Keywords: wirelesscapsuleendoscopy;featureselection;colorspaceselection;statisticalanalysis;
supportvectormachine;gridsearch;overlappingarea;classification
1. Introduction
Gastrointestinaltract(GIT)diseases,suchasulcer,bleeding,Crohn’sdisease,cancerorchronic
diarrhea are common nowadays. Bleeding and ulcer are some common lesions which affect the
small and large bowel. In the United States, approximately 1.6 million Americans currently are
currentlysufferingfrominflammatoryboweldisease(IBD),representinganincreaseofabout200,000
since2011.Thereareapproximately70,000newcasesofIBDdiagnosedeachyear,andtheremaybe
as many as 80,000 children who are suffering from Crohn’s disease (CD) or ulcerative colitis (UC)
currently. Additionally,asillustratedreportedinthefirstpaperofthisissue[1],theincidencenumber
ofoccurrencesofIBDisincreasingworldwide[2]. ThegrowthofIBDcasesinnewly-industrialized
countrieshasparalleleditsgrowthonparwiththatoftheWesternworld30to40yearsago.Geneticand
environmentalstudiesperformedinthesecountriesmayprovidenewcluestothepathogenesisofIBD.
However,itaddsanotherlayerofcomplexitysinceriskfactorsandgene-environmentinteractions
mayvarybycontinentsandethnicities[3]. Traditionalendoscopyhasbeenadoptedformanyyearsin
ordertodiagnoseabnormalitiesofGIT,wherebyaphysiciancontrolsaflexibleendoscopetoexamine
the lower and upperparts of GIT. Thistechnique is limited to inspecting bowelof average length
7–8m. Itimposeshighlevelofdiscomfortonthepatientaswell.
Wirelesscapsuleendoscopy(WCE)[4,5]isarecenttechnologyintroducedbyGivenImaging
Ltd. (Yokne’amIllit,Israel)tovisualizetheentireGITpainlessly. Itoffersanefficientandcomfortable
Appl.Sci.2017,7,1097;doi:10.3390/app7101097 www.mdpi.com/journal/applsci
Appl.Sci.2017,7,1097 2of12
way for visualizing the complete GIT. It has eight skin antennas mounted to the abdomen wall.
WhilemovingthroughthecompleteGIT,thecapsulecapturesnumerousimagesatapproximately
2–4framespersecond(fps), andtransfersthemwirelesslytothedatalogger(DL)orrecorderunit.
ThisDLishookeduptothepatientwaistandthevideos/imagesarestored. Oncetheexaminationis
complete(i.e.,theWCEexitspatient’sbodyafter8h),theimagescanbedownloadedtoadedicated
computer from DL and inspected by clinical experts through specific software. This procedure
producesmorethan60,000imagesperexaminationandexpertsspendabout4–5htoinspectthewhole
videofootageverycarefully. Insomemildcases,clinicianshavetogothrougheachframemanually,
leadingtovisualfatigue. Thistediousandtime-consumingprocessisthemaindrawbackofWCE.
Variousimageprocessingtechniquesforautomaticdiseasedetection(basedonsize,shape,anddepth,
includingperformancematrices)havebeendeveloped. Automaticlesiondetection,ontheotherhand,
ismoreefficientforchroniccases.
In this particular work, we have extracted color features for various color spaces. We have
analyzedeachbandinordertoseparatetheulcerandnon-ulcerpixels. Furthermore,itwascombined
withcross-correlationmeasureinordertoaddsimilaritybetweentwoimagesormatrices.Classification
taskwasperformedusingsupportvectormachinewithandwithoutgridsearchmethodinorderto
quantifytheresult.Themaincontributionofthisworkistheimplementationofanovelcomputer-aided
diagnostic method which is used to discriminate ulcer pixels from non-ulcerated ones with high
performanceintermsofsensitivity,specificity,andaccuracy.
Thispaperisorganizedinthefollowingmanner: Section2describestheresearchbackground
andtherelatedworks. Section3demonstratesthemethodology,andSection4describesexperimental
setup,results,anddiscussions. Section5concludesthecurrentwork.
2. BackgroundandLiterature
Researchershaveventuredintothetechnologyofusingautomatedcomputer-aideddesign(CAD)
tools,suchasWCEforulcerscreening. Preciousclinicalinformationinsomeimportantlesionareas
canbedisplayedonanimage[6–9]. Deeba[6]hasusedRetinextheoryandthesalientregiondetection
methodforvariouspathologies,suchasstenosis,chylouscysts,lymphangiectasis,polypoid,bleeding,
angioectasia,andulcers. Theyhaveusedacolorenhancementmethodtoimprovethediagnosticyield
oftheCADsystem. AsignificantimprovementindetectionperformanceusingtheRetinex-based
colorenhancementmethodhasbeenachieved. Anunsupervisedmethod[10]hasbeenusedtolocalize
theregion-of-interestinordertodetectangioectasia. TheutilityofIHbindexforangioectasiadetection
hasbeenpioneeredtodetectotherabnormalities.
Yuan[8]hasusedasaliencydetectionmethodbasedonmulti-levelsuperpixelrepresentationin
ordertooutlinetheulcercandidates. Theyhaveevaluatedthecorrespondingsaliencyinaccordance
withthetextureandthecolorfeatureofeachlevelinthesuperpixelregion. Theimageshavebeen
categorizedbyusingsaliencymax-poolingwithlocality-constrainedlinearcoding(LLC).
Iakovidis [9] has presented a method to detect various abnormalities in GIT by considering
color as a discriminative feature. This method has been tested on a WCE model. Here, the single
image(insteadofthecompleteWCEvideofootage)hasbeenanalyzed. Theauthor[11]hasreviewed
some current CAD methods employed in enhanced video capsule endoscopy. Various hardware
and software problems have been highlighted in a review article [12] for detecting lesions in the
smallbowel.
Pepticulcersareusuallyfoundintheduodenum(duodenalulcer)andinthestomach(gastric
ulcer). TheycanbefoundinsmallandlargebowelareasoftheGITaswell. Theymaycausesevere
gastrointestinalperforationorgastrointestinalbleeding[13]. Usually,itappearsasawhitespotin
WCE images. In severe cases, these ulcers are accompanied by bleeding and other abnormalities.
Ulcerlesionsandnormaltissuecanbedifferentiatedbyusingcolorandtexturefeatures(seeFigure1).
Appl.Sci.2017,7,1097 3of12
Appl. Sci. 2017, 7, 1097 3 of 12

Figure1.WCEimageswithbleedingandulcer.(a,b)Ulcerimage;and(c,d)bleedingimages.
Figure 1. WCE images with bleeding and ulcer. (a,b) Ulcer image; and (c,d) bleeding images.
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ainnfaolrymseastioofn.u Ilnce oruarn pdrnevoino-uusl cweroartke d[1p9i]x, eRlsG.BT ahnisd pLaapbe rcoelxotre nspdasctehse hwavoer kbeoefn[ 1c9h]obseyni nfocor rsptaotriasttiicnagl
vaanrailoyussesc oolfo rublcaenrd asnfdo rnsotant-iustliccearlaatenda lypsixise.ls. This paper extends the work of [19] by incorporating
varioFuisg ucoeilroerd boaentdasl .f[o2r0 s]thaatisstpircoapl oanseadlyasigse. ometry-basedautomaticcolorectalpolypdetectionmethod
that hFaigsumeioretdivoa teetd atlh. e[2c0u]r rheanst pwroorpko.seTdh ea aguetohmoerstry[2-1b]asheadv eauptroompoatsiecd coalonreewctaclo mpopluypte rd-aestesicsttieodn
bmleeethdoindg thdaett ehcatso rmfootrivdaiftfeedr etnheti actuinrrgenbte wtwoerekn. Tbhleee aduinthgoarns d[2n1]o hna-bvlee epdroinpgosreegdi ao nnse.wT hcoemutpiluizteerd-assescisotnedd
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aconmisoptoronpenict dfrifofmus itohne. CIE Lab color space with enhancement and segmentation techniques involves
anisotropic diffusion.
3. Methodology
3. Methodology
Whileanalyzingthefeatures,itisnecessarytoperformafewpriorstepswhicharerelatedto
imagWe phriloec easnsainlygz.inTgh itshes efcetaitounressh, oiwt iss tnheececsosmarpyl ettoe pfleorwforomf wa oferkw. pTrhioerp srtoeppos swedhiwcho rakref orceulasteesdo tno
rimedaugcei npgrothceesasinnagl.y sTihsitsi mseectwiohni lsehporwocse tshsein cgohmupgleetne ufmlobwe roof fwimorakg. eTsh.eR pGrBopcoosloerd swpaocrek ifsotchuesems oosnt
preodpuuclianrgc othloer asncahleymsise tinimvei swuahlilsey pstreomces[2si2n];gh houwgeev neur,mitbhear soaf fimewagmesa.j oRrGdBis caodlvoar nstpaagcees ifso rthnea tmuoraslt
ipmoapguelsarw choelroer hsicghhemcoer rienl avtiiosunabl estywseteemn c[o2m2]p; ohnoewnetsvecar,n itb ehaosb sae rfevwed m[2a3j]o.rF digisuardev2adnetapgicetss ftohre nflaotwuroafl
mimeathgoesd owlohgeyref ohrigfhea ctourrreeclalatisosnifi bcaettwioeneann cdoemxptroancteinotns. can be observed [23]. Figure 2 depicts the flow
of methodology for feature classification and extraction.
Appl.Sci.2017,7,1097 4of12
Appl. Sci. 2017, 7, 1097 4 of 12

Figure 2. Methodology of proposed feature selection and classification.
Figure2.Methodologyofproposedfeatureselectionandclassification.
3.1. Image Processing and Enhancement
3.1. ImageProcessingandEnhancement
The video generated after WCE examination is usually saved in a raw format and it cannot be
ThevideogeneratedafterWCEexaminationisusuallysavedinarawformatanditcannotbe
processed directly by any programming platform. Endocapsule software (MAJ-2039) (EC 10, Olympus,
processeddirectlybyanyprogrammingplatform. Endocapsulesoftware(MAJ-2039)(EC10,Olympus,
Seri Iskandar, Perak, Malaysia, 2014) was used to import raw video in Audio Video Interleave (AVI)
SeriIskandar,Perak,Malaysia,2014)wasusedtoimportrawvideoinAudioVideoInterleave(AVI)
format. Subsequently, MATLAB was used to extract all images from the entire video footage in
format. Subsequently,MATLABwasusedtoextractallimagesfromtheentirevideofootageintagged
tagged image file format (TIFF) format. We have chosen the TIFF format to represent the WCE color
imagefileformat(TIFF)format. WehavechosentheTIFFformattorepresenttheWCEcolorimage
image because it has greatest strength to support the full range of image size, color depth, and
becauseithasgreateststrengthtosupportthefullrangeofimagesize,colordepth,andresolution.
resolution. It also supports various compression techniques, where lossless compression allows this
Italsosupportsvariouscompressiontechniques,wherelosslesscompressionallowsthisformatto
format to maintain image resolution without loss of any detail related to image [24]. Image
maintain image resolution without loss of any detail related to image [24]. Image enhancement
enhancement is essential before applying any techniques in image processing. It is a technique that
is essential before applying any techniques in image processing. It is a technique that is able to
is able to eliminate non-essential information from an image [25]. In this particular work, we have
eliminatenon-essentialinformationfromanimage[25]. Inthisparticularwork,wehaveusedwavelet
used wavelet de-noising [26] with three levels of decomposition. Additionally, the db2 wavelet with
de-noising[26]withthreelevelsofdecomposition.Additionally,thedb2waveletwithsoftthresholding
soft thresholding method was applied to eliminate redundant noise.
methodwasappliedtoeliminateredundantnoise.
3.2. Feature Extraction and Feature Selection
3.2. FeatureExtractionandFeatureSelection
In this section, we have generated various normalized color spaces for feature analysis.
Inthissection,wehavegeneratedvariousnormalizedcolorspacesforfeatureanalysis. RGB,
RGB, HSV, YCbCr, CIE Lab, YUV, XYZ, and CMYK color spaces have been chosen for feature
HSV,YCbCr,CIELab,YUV,XYZ,andCMYKcolorspaceshavebeenchosenforfeatureextraction
extraction and feature selection. By using these secen color spaces, we have analyzed 22 separate
andfeatureselection. Byusingthesesecencolorspaces,wehaveanalyzed22separatecolorbands
color bands individually. These bands contain various information of lesion and it is essential to
individually. Thesebandscontainvariousinformationoflesionanditisessentialtoidentifythebest
identify the best band for the classification step.
bandfortheclassificationstep.
We have used two methods to identify the best suitable band by separating foreground (non-
We have used two methods to identify the best suitable band by separating foreground
ulcerated) and ulcer pixels. These methods involve statistical analysis of foreground and ulcer pixels
(non-ulcerated)andulcerpixels. Thesemethodsinvolvestatisticalanalysisofforegroundandulcer
in each band. We have implemented the first method (or Method 1) named as the overlapping area
pixelsineachband. Wehaveimplementedthefirstmethod(orMethod1)namedastheoverlapping
(OA) to measure the overlapping area between ulcer and foreground pixels. The normal distribution
area (OA) to measure the overlapping area between ulcer and foreground pixels. The normal
curve for foreground and ulcer pixels is analyzed from each pixel set. Here, we are able to highlight
distributioncurveforforegroundandulcerpixelsisanalyzedfromeachpixelset. Here,weareableto
the index of separation for each band which shows overlapping area between foreground and ulcer
highlighttheindexofseparationforeachbandwhichshowsoverlappingareabetweenforeground
pixels. Of course, lesser overlapping area signifies better separation in a particular band. The second
method (or Method 2) uses overlapping area with cross-correlation (OACCorr) value in each band.
Cross-correlation [27] is a standard measure for determining similarity between two images or
Appl.Sci.2017,7,1097 5of12
andulcerpixels. Ofcourse,lesseroverlappingareasignifiesbetterseparationinaparticularband.
Thesecondmethod(orMethod2)usesoverlappingareawithcross-correlation(OACCorr)valuein
eachband. Cross-correlation[27]isastandardmeasurefordeterminingsimilaritybetweentwoimages
ormatrices. Itismoreaccurateandcomputationallymoreefficient. Additionally,itdependsonthe
calculationofcovariancebetweentwobands.
The method of fast normalized cross-correlation is fast, independent, and accurate (in terms
ofpixelcontrastandbrightnessvalues)incomputingimagesimilarity. Thistechniqueinvolvesthe
estimation of correlation between all bands in the data cube of band i. Equation (1) was used to
estimatethecorrelationvaluesofotherbandsinjdatacube:
∑ (cid:2)(cid:0)D(x,y)−D(cid:1)×(cid:0)D (x,y)−D (cid:1)(cid:3)
xy i i j j
NCCi,j = (cid:113) (cid:113) (1)
∑ (cid:0)D(x,y)−D(cid:1)2× ∑ (cid:0)D (x,y)−D (cid:1)2
xy i i xy j j
where NCC isthenormalizedcross-correlationbetweenbandiandj, D(x,y). istheintensityof
i,j i
pixel,(x,y)arethepixelindiceswithinoneband,andD isthemeanofpixelintensityvaluesofbandi.
i
Table1showstheOAandOACCorrvaluesfor22colorbandsusedinextractingthecolorfeatures.
We have used seven color spaces in the following order: RGB, HSV, YCbCr, CIE Lab, YUV, XYZ,
andCMYK.ThefirstcolorspaceisRGBwithrgbR,rgbG,andrgbBcolorbandswithallottedband
numbers of 1, 2 and 3, respectively. The second color space is HSV with hsvH, hsvS, and hsvV
color bands. Their allotted band numbers are 4, 5 and 6, respectively. The third color space is
YCbCrwithycbcrY,ycbcrCB,andycbcrCRcolorbands. Theirallottedbandnumbersare7,8and9,
respectively. ThefourthcolorspaceisCIELabwithCIELabL,CIELabA,andCIELabBcolorbands.
Theirallottedbandnumbersare10,11and12,respectively. ThefifthcolorspaceisYUVwithyvuY,
yvuU,andyuvVcolorbandsofallottedbandnumbers13,14and15,respectively. Thesixthcolorspace
isXYZwithxyzX,xyzY,andxyzZcolorbandsofallottedbandnumbers16,17and18,respectively.
Finally,theseventhcolorspaceisCMYKwithfourbands,i.e.,cmykC,cmykM,cmykY,andcmykK.
Theirallottedbandnumbersare19,20,21and22,respectively. Theseentirebandsareusedasfeature
vectorforfurtherclassification.
FromTable1(columns2and3),theoverlappingareasoftherespectivebandnumberandband
nameobtainedusingMethod1aredisplayedinanincrementalmanner. ForMethod2(seecolumns4
and5inTable1),theoverlappingareasaddedwithstandardmeasureofdegreeofsimilaritybetween
twoimagesaredisplayedinanincrementalmanneraswell. Forexample,iftheserialnumber1for
featureextractionisanalyzedusingMethod1,itshowsachromiumcomponent(ycbcrCR)ofYCbCr
withbandnumber9thathasalesseroverlappingareabetweenulcerandforegroundpixels. Ifthe
resultofMethod2isanalyzed,thesameband(ycbcrCR)isselectedaswell.
Table1.Comparisonofoverlappingareawithoverlappingareaandcross-correlationmethod.
OA OACCorr
SerialNo.
BandNo. BandName BandNo. BandName
1 9 ycbcrCR 9 ycbcrCR
2 11 CIELabA 21 cmykY
3 4 hsvH 11 CIELabA
4 17 yuvU 4 hsvH
5 21 cmykY 3 rgbB
6 3 rgbB 17 yuvU
7 15 xyzZ 5 hsvS
8 5 hsvS 15 xyzZ
9 20 cmykM 8 ycbcrCB
10 16 yuvY 20 cmykM
11 8 ycbcrCB 16 yuvY
12 12 CIELabB 12 CIELabB
Appl.Sci.2017,7,1097 6of12
Table1.Cont.
OA OACCorr
SerialNo.
BandNo. BandName BandNo. BandName
13 19 cmykC 19 cmykC
14 22 cmykK 22 cmykK
15 1 rgbR 1 rgbR
16 6 hsvV 6 hsvV
17 13 xyzX 13 xyzX
18 2 rgbG 2 rgbG
19 7 ycbcrY 7 ycbcrY
20 14 xyzY 14 xyzY
21 18 yuvV 18 yuvV
22 10 CIELabL 10 CIELabL
3.3. MachineLearning
SVMs are accurate as they contain appropriate kernels that work well even if the data is not
linearlyseparableinthebasefuturespace. ByusingthekernelfunctionsofSVM[28],onecanperform
anon-linearclassificationmoreaccuratelybymappingitsinputtohigh-dimensionalfeaturespaces[29].
Therearevarioushyperplanesthatseparatetheclasses; however, itisimportanttoselectthebest
one which has the largest distance to the nearest data point of two classes. Grid search [30] is the
conventionalmethodofperformingtheoptimizationofhyperparameterutilizingparametersweep
orgridsearchthroughamanuallyspecifiedsubsetofthehyperparameterofalearningalgorithm.
Thisalgorithmmustbeguidedbysomeperformancemetric,normallymeasuredbyevaluationon
aheld-outvalidationsetorbycross-validationofthetrainingdataset. Forthiswork,weareusing
anSVMclassifierwithanRBFkernelhavingatleasttwoparameters(regularizationconstantCand
kernel hyper parameter γ) that need to be tuned to achieve high performance on the testing data.
The mathematical descriptor is shown below for a binary classification problem: {(x , y ), (x , y ),
1 1 2 2
... , (x , y )}, where x ∈ R represents the n-dimensional feature vectors, and y ∈ {1, −1} is the
k k i n i
correspondingclasslabel. TheSVMrequiresthesolutionofthefollowingoptimizingproblem:
k
min( 1ωTω + C ∑ ε )
2 i (2)
i=1
Subjectedtoy (ωTϕ(x )+b) ≥1−ε , ε ≥0, i =1, ... ,k
i i i i
Here,ε istheslackvariableformisclassifiedexamples,andCisthepenaltyparameteroferror
i
term. Inaddition,K(x,x)= ϕ(x)Tϕ(x)isthekernelfunction. Basically,therearefourkernelfunctions
i j i j
usedforpatternrecognitionandclassification: linearkernel,polynomialkernel,radialbasisfunction
(RBF),andsigmoidkernel. WehaveadoptedtheRBF[28]kernelinthispaper:
K(cid:0)x, x (cid:1) =exp(−γ (cid:107) x −x (cid:107)2), γ > 0 (3)
i j i j
Here,γistheparameterwhichmustbecarefullyselectedintheexperiment. Theoptimumvalues
forparameterCandlog γwereselectedfromtherange: (−8,7,6,... ,6,7,8). Thegridmethod[29]
2
wasadoptedasthesearchingprocedure(a0.8stepwasused). EachγandCvaluepairwasusedin
thetrainingdatawithten-foldcross-validationinordertoevaluatethemodelperformance. Oncethe
optimalvaluesofγandCwerefound,theywereadoptedtotrainanewSVMmodel.
4. ExperimentalResultsandDiscussions
ThisworkwasassistedbytheexpertisefromtheendoscopyunitinUniversityofMalayaMedical
Centre(UMMC),KualaLumpur,Malaysiaformedical/clinicaladvises. TheUTPteam,ontheother
hand,wasresponsibleinprovidingtheengineeringsolutions. Weaccumulated30videosofvarious
Appl.Sci.2017,7,1097 7of12
abnormalities. Moreover, the experts provided us the ground truth for these videos with labelled
ulceratedlesions. Thisdatasetservesasthereferencedatasetforoursubsequentanalysis. TheWCE
pillusedforgeneratingthedatasetwasEndocapsuledevelopedbyOlympus. Theresolutionofthe
datasetprovidedwas288×288. TheprocessorusedforthisworkwasIntel(R)Core(TM)i7-2600CPU
@3.20GHz(DellOptiplex990,SeriIskandar,Malaysia)with8GBmemory. Thechosenprogramming
platformwasMATLABR2017a(Matlab9.2,MathWorks,Malaysia,2017).
Appl. Sci. 2017, 7, 1097 7 of 12
4.1. DatasetSelection
4.1. Dataset Selection
Ourdatasetconsistedof48,000WCEimages. Theseimages(16,000ofthem)weredividedinto
Our dataset consisted of 48,000 WCE images. These images (16,000 of them) were divided into
threegroups. Ineachgroup,weused8000imagestocreateourtrainingset(5000imageswereulcer
three groups. In each group, we used 8000 images to create our training set (5000 images were ulcer
samplesand3000imageswerenormalsamples)and8000imagesintestingset(5000imageswereulcer
samples and 3000 images were normal samples) and 8000 images in testing set (5000 images were
samplesand3000imageswerenormalsamples). Theseimageswereaccumulatedfrom30patients
ulcer samples and 3000 images were normal samples). These images were accumulated from 30 patients
andmanually-labelledbygastroenterologists.
and manually-labelled by gastroenterologists.
4.2. ResultsofStatisticalAnalysis
4.2. Results of Statistical Analysis
TheresultsarepresentedinTable1.
The results are presented in Table 1.
FromTable1,itisapparentthattheoverlappingareamethod(Method1)canbetterrevealthe
From Table 1, it is apparent that the overlapping area method (Method 1) can better reveal the
ulcer information with the following bands (arranged in the descending performance): ycbcrCR,
ulcer information with the following bands (arranged in the descending performance): ycbcrCR,
cmykY, CIE LabA, hsvH, rgbB, yuvU, and CIE LabL. Similarly, by using overlapping area with
cmykY, CIE LabA, hsvH, rgbB, yuvU, and CIE LabL. Similarly, by using overlapping area with cross-
cross-correlation(Method2),bandssuchasycbcrCr,cmykY,rgbB,andhsvScontainsmoreinformation
correlation (Method 2), bands such as ycbcrCr, cmykY, rgbB, and hsvS contains more information for
foruulclceer rlelesisoionn. .InIn FFigiguurer e3,3 ,wwe ehhaavve eexetxrtarcatcetde dthteh efefaetautruer evevcetoctro frofro eraecahc hbabnadn dusuinsgin MgMetheothdo 1d a1nadn d
MeMtheothdo2d. 2B. Byyc chhoooossiinngg 5500%% ooff oovverelralpappipnign agreaar eaanda ncdroscsr-ocsosr-rceolarrtieolnat, iiot ngi,vietsg uivs esisxu csolsoirx bcaonldosr sbuacnhd s
sucahs ayscbyccrbCcRr,C cRm,yckmYy, kCYIE, CLaIbEAL,a hbsAvH,h, srvgbHB,, ragnbdB y,uanvUd y(iu.ev.,U th(ei .fei.r,stth seixfi orusttpsuixt roouwtps uotf rToawblse o1f) Tuasibnlge 1)
usiMngetMhoedth 1o dan1da fnodurf ocuolrocro bloanrdbsa nsudcshs uasc hycabscyrCcbr,c rcCmry,kcmY,y rkgYb,Br gabnBd ahnsvdSh (si.veS., (tih.ee. ,fitrhset fiforusrt froouwrsr ionw s
inTTaabbllee 11)) uussiinngg MMeetthhoodd 22.. WWee ffoouunndd tthhaat ttthhee ppeerrfoformrmanancec eclcalsassifsiiefiresr ssuscuhc hasa ssesnesnitsivitiitvyi,t ysp,sepcieficciifityc,i ty,
andanadc cauccraucryacayr aersei msimilailrawr whhenenm mooreret hthaann 5500%% ooff oovveerrllaappppiinngg aarreeaa aanndd crcorosss-sc-ocrorrerlealtaiotnio anrea rcehcohseons.e n.
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1.2
s Overlapping Area
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FiFgiugruere3 .3C. Comomppaarirsiosonno offo ovveerrllaappppiinngg aarreeaa aanndd WWeeiigghhtteedd ccoorrrreelalatitoionn vvalaulue.e .
The x-axis in Figure 3 shows the color bands for Method 2 (i.e., similar to column 5 in Table 1).
Thex-axisinFigure3showsthecolorbandsforMethod2(i.e.,similartocolumn5inTable1).
A total of 50% of overlapping area (six feature vectors) and cross-correlation (four feature vectors) is
Atotalof50%ofoverlappingarea(sixfeaturevectors)andcross-correlation(fourfeaturevectors)is
taken as feature vector for Method 1 and Method 2. For Method 1 (OA), data on blue line containing
takenasfeaturevectorforMethod1andMethod2. ForMethod1(OA),dataonbluelinecontaining
ycbcrCR, cmykY, CIE LabA, hsvH, rgbB, and yuvU are chosen as feature vector as they have less
overlapping area between ulcer and foreground pixels. For Method 2 (OACCorr), data on red line
containing ycbcrCR, cmykC, rgbB, and hsvS are chosen as feature vector the overlapping areas added
with standard measure of degree of similarity between two images are displayed in an incremental
manner. The selected feature vectors were further fed into the classifier in order to analyze the
algorithm performance in terms of sensitivity, specificity, and accuracy.
Appl.Sci.2017,7,1097 8of12
ycbcrCR, cmykY, CIE LabA, hsvH, rgbB, and yuvU are chosen as feature vector as they have less
overlappingareabetweenulcerandforegroundpixels. ForMethod2(OACCorr),dataonredline
containingycbcrCR,cmykC,rgbB,andhsvSarechosenasfeaturevectortheoverlappingareasadded
withstandardmeasureofdegreeofsimilaritybetweentwoimagesaredisplayedinanincremental
manner. The selected feature vectors were further fed into the classifier in order to analyze the
algorithmperformanceintermsofsensitivity,specificity,andaccuracy.
Thesebandsprovidethebestresultintermsofseparatingtheulceratedandnon-ulceratedpixels.
Toenhancetheresultofclassification,wefedtheselectedbandtotheSVMclassifier. Inthisclassifier,
weusedradialbasisfunction(RBF)askernelfortheSVM.Theresultofclassificationforperformance
matricesisshowninTable2.
Table2.Sensitivity,specificity,andaccuracyusingSVMclassifier.
Methods ColorBands
Cr,Y,A,H,B,U Cr,Y,A,H,B Cr,Y,A,H
Sen Spe Sen Spe Sen Spe
Method1 SVM(OA)
90.32 90.55 91.98 91.56 91.58 91.23
Cr,Y,B,S Cr,Y,B Cr,Y
Sen Spe Sen Spe Sen Spe
Method2 SVM(OACCorr)
92.58 91.84 93.76 92.91 93.08 91.36
4.3. PerformanceMetrics
Performancemetricssuchasaccuracy(Acc),sensitivity(Sen),andspecificity(Spe)werecomputed
toevaluatetheeffectivenessoftheproposedmethod. FortheexperimentationonGITimagedata
set,thepositivesamplerepresentslesionimagesandthenegativesamplerepresentsnormalimages.
Theequationsforsensitivityandspecificitycanbeexpressedas:
Accuracy(Acc)=TP+TN/(TP+TN+FP+FN) (4)
Sensitivity(Sen)=TP/(TP+FN) (5)
Specificity(Spe)=TN/(FP+TN) (6)
where TP represents the number of true positives, TN represents the number of true negatives,
FPrepresentstheincorrectpositiveimagesamplesidentifiedandFNrepresentstheincorrectnegative
imagesamplesidentified. Fromtheclinicalexperts,sensitivitydefinestheprobabilityofapositive
analysisandspecificityistheprobabilityofanegativeanalysis.
InTable2,SVM(OA)isdepictedasSupportvectormachineforoverlappingarea. Itiscomputed
for seven bands, six bands, and five bands and the results are shown in Table 2. Similarly, SVM
(OACCorr)isasupportvectormachineforoverlappingareawithcross-correlationvalueanditis
computedforfourbands, threebands, andtwobands. Here, Cr, Y,A,H andBgivebetterresults
forMethod1(OA).Ontheotherhand,Cr,YandBgivethebestresultwithMethod2(OACCorr).
Specifically,Method2ismorepromisingthanMethod1. Theaboveresultsshowsthatcombining
variouscolorbandscanprovidemoremeaningfulresults.
Figure4showstheanalysesofSVM(OACCorr)byusinggridsearchmethod(WGS)andwithout
gridsearchmethod(WOGS).Theaccuraciesof3datasetshavebeencompared. Fromtheresultstaken
fromCr,Y,Bcolorbands,theextractedfeatureismoreobvioususinggridsearchmethod. Forthis
method,theaverageaccuracy,sensitivity,andspecificityare97.89%,96.22%and95.09%,respectively.
Additionally,wehavecomparedourresultsofclassificationwithothersaspresentedinTable3.
Itisimportanttonotethedatasetprovidedbyotherauthorsmightbeextractedfromdifferentcapsule.
Therefore,forcomparisonpurpose,thedatasetemployedinthecurrentworkhasbeenusedforall
Appl.Sci.2017,7,1097 9of12
methods, including those reported by other researchers (Table 3 shows the dataset used by other
authors). Theauthorsin[31]combinedthemeritsofbothContourlettransformandLogGaborfilterin
HSVcolorspace;however,theirdatasetwasverysmall. Authorfrom[13]utilizedRGBandHSVcolor
spaceandclassificationwasperformedusingMLPneuralnetwork. Byusingcolorcoherencevector
(CCV),apromisingresultwasattained. Asreportedin[32],colorwaveletcovariancefeaturewasused
onvariouscolorspaces,andTextonboostwasappliedtoclassifynormalandabnormaltissues. Itis
interestingtonotethattheproposedmethodshowshighersensitivityandhigherspecificityevenfora
vAeprpyl. Slacir. g20e1d7,a 7t,a 1s0e9t7. 9 of 12
98
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Sen Spe Sen Spe
WGS SVM WOGS SVM

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Author Color Space/Bands Classifier Dataset Acc(%) Sen (%) Spe(%)
Author ColorSpace/Bands Classifier Dataset Acc(%) Sen(%) Spe(%)
[31] HSV SVM 137 images 94.83 91.89 97.16
[31] HSV SVM 137images 94.83 91.89 97.16
[13] RGB, HSV, CCV MLP 448 images 86.93 89.03 85.56
[13] RGB,HSV,CCV MLP 448images 86.93 89.03 85.56
[3[322]] VVaarriioouuss ccoolloorrs sppacaece JoJionitnbto boostost 10100i0m iamgaesges NNAA 919.16.767 848.47.373
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ppiixxeellss. fGrormid nmoertmhoadl phiaxselbse. eGnriadd ompettehdoads hthase bseeaernc hadinogppterdo caesd tuhree s(e0a.8rcshteinpgw parsocuesdedur).e E(0a.c8h sγteapn wdaCs
vuasleude).p Eaairchh aγs abneden Cu vseadluien ptahier thraasin bineegnd uatsaedw iinth thteen t-rfaoilndincrgo dssa-tvaa wliditaht itoenn-ifnolodr dcerorstso-veavlaidluaatitoenth ien
order to evaluate the model performance. A large dataset has been used to create training and testing
dataset in order to obtain meaningful result. In this work, the sensitivity and the specificity of ulcer
classification using the grid search method in SVM are 96.22% and 95.09%, respectively, which are
substantially higher than those of classification without grid search method (93.76% and 92.91%,
respectively). The main contribution of this work is the implementation of a novel computer aided
diagnostic method which can be used to discriminate ulcer pixel from non-ulcerated pixel.
The method exhibits promising performance in terms of sensitivity, specificity, and accuracy.
The current work has paved the way to providing a reliable computer-aided WCE diagnosis system.
Appl.Sci.2017,7,1097 10of12
modelperformance. Alargedatasethasbeenusedtocreatetrainingandtestingdatasetinorderto
obtainmeaningfulresult. Inthiswork,thesensitivityandthespecificityofulcerclassificationusing
thegridsearchmethodinSVMare96.22%and95.09%,respectively,whicharesubstantiallyhigher
thanthoseofclassificationwithoutgridsearchmethod(93.76%and92.91%,respectively). Themain
contributionofthisworkistheimplementationofanovelcomputeraideddiagnosticmethodwhich
can be used to discriminate ulcer pixel from non-ulcerated pixel. The method exhibits promising
performanceintermsofsensitivity,specificity,andaccuracy. Thecurrentworkhaspavedthewayto
providingareliablecomputer-aidedWCEdiagnosissystem.
Acknowledgments:ThisresearchworkissupportedbytheCentreforIntelligentSignalandImagingResearch
(CISIR)andtheGraduateAssistantship(GA)scheme,UniversitiTeknologiPETRONAS,Perak,Malaysia.
AuthorContributions:ShipraSumanfinishedthedraftofthispaper.FawnizuAzmadiHussincontributedin
thedesignoftheexperimentalwork.AamirSaeedmalikcontributedinfeatureextractionandfeatureselection
algorithmforbetterdiscriminationbetweenulcerandforegroundpixel.ShiawHooiHoandIdaHilmicontributed
inthelabellingofUlcerdatasetusedintheexperiment. AlexHwong-RueyLeowandKhean-LeeGohhave
contributiontowardsdiseasespecificationandotherchallengesfoundthroughoutthewritingandotherreviewing
procedure.Allauthorsreviewedandapprovedthecontentsofthispaper.
ConflictsofInterest:Theauthorsdeclarenoconflictofinterest.
Abbreviations
AVI AudioVideoInterleaved
CAD ComputerAidedDesign
CCV ColorCoherenceVector
DL DataLogger
GIT GastrointestinalTract
GLCM GreyLevelCo-OccurrenceMatrix
LLC Locality-constrainedLinearCoding
OA OverlappingArea
OACCorr OverlappingareawithCross-correlation
RBF RadialBasisFunction
SVM SupportVectorMachine
WCE WirelessCapsuleEndoscopy
WGS WithGridSearch
WOGS WithoutGridSearch
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