Data mining framework for breast lesion classification in shear wave ultrasound: A hybrid feature paradigm

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Biomedical Signal Processing and Control

j ou rn a l h o m e pa g e :w w w . e l s e v i e r . c o m / l o c a t e / b s p c

Research paper

Data mining framework for breast lesion classification in shear wave ultrasound: A hybrid feature paradigm

U. Rajendra Acharya

^a,b,c

, Wei Lin Ng

^d

, Kartini Rahmat

^d

, Vidya K. Sudarshan

^a,∗

, Joel E.W. Koh

^a

, Jen Hong Tan

^a

, Yuki Hagiwara

^a

, Chai Hong Yeong

^d

, Kwan Hoong Ng

^d

aDepartmentofElectronicsandComputerEngineering,NgeeAnnPolytechnic,Singapore

bDepartmentofBiomedicalEngineering,SchoolofScienceandTechnology,SIMUniversity,Singapore

cDepartmentofBiomedicalEngineering,FacultyofEngineering,UniversityofMalaya,Malaysia

dDepartmentofBiomedicalImaging,FacultyofMedicine,UniversityofMalaya,Malaysia

a r t i c l e i n f o

Articlehistory:

Received3August2016

Receivedinrevisedform12October2016 Accepted2November2016

Availableonline30December2016 Keywords:

Elastography

Shearwaveelastography Benignbreastlesions Malignantbreastlesions Nonlinearfeatures

a b s t r a c t

Assessmentofelasticityparametersofbreastusingultrasoundelastography(USE)providesexclusive informationaboutthecanceroustissue.Shearwaveelastography(SWE),anewUSEimagingprocedure isincreasinglyusedforelasticityevaluationofbreastlesions.SWEexaminationisgainingpopularity inthecharacterizationofbenignandmalignantbreastlesionsasithashighdiagnosticperformance accuracy.However,somedegreeofmanualerrors,suchasprobecompressionormovementmaycause inaccurateresults.Inaddition,thesystemscannotmeasureelasticityvaluesinsmalllesionswherethe tissuesdonotvibrateenough.Thus,computer-aidedmethodssuppressthesetechnicalormanuallimi- tationsofSWEduringevaluationofbreastlesions.Therefore,thispaperproposes,anovelmethodology forcharacterizationofbenignandmalignantbreastlesionsusingSWE.OriginalSWEimageissubjected tothreelevelsofDiscretewavelettransform(DWT)toobtaindifferentcoefficients.Secondorderstatis- tics(RunLengthStatistics)andHu’smomentsfeaturesareextractedfromDWTcoefficients.Extracted featuresaresubjectedtosequentialforwardselection(SFS)methodtoobtainthesignificantfeatures andrankedusingReliefFfeaturerankingtechnique.Rankedfeaturesarefedtodifferentclassifiersfor automatedcharacterizationofbenignandmalignantbreastlesions.Ourproposedtechniqueachieveda significantaccuracyof93.59%,sensitivityof90.41%andspecificityof96.39%usingonlythreefeatures.In addition,auniqueintegratedindexnamedShearWaveBreastCancerRiskIndex(sBCRI)isformulated forcharacterizationofmalignantandbenignbreastlesionusingonlytwofeatures.Theproposedindex, sBCRI,providesasinglenumberwhichcharacterizesthemalignantandbenigncancerfaster.Thissystem canbeemployedasanidealscreeningtoolasithashighsensitivityandlowfalse-positiverate.Hence, thewomenwithbenignlesionsneednotundergounnecessarybiopsies.

©2016ElsevierLtd.Allrightsreserved.

1. Introduction

Breastcancerisoneoftheleadingcancers(malignancies)in womenandthesecondprimarycauseofcancer-associateddeaths [11,56,119].AccordingtoWorldHealth Organization(WHO), in 2011,more than 508,000 breast cancerdeaths are reported in womenworldwide[112].Itisreportedthat,1.8millionwomen arediagnosedwithbreastcancerworldwidein2013[48]anditis projectedthatin2016,intheUnitedStatesofAmerica(USA)alone,

∗Correspondingauthorat:DepartmentofElectronicsandComputerEngineering, NgeeAnnPolytechnic,599489,Singapore.

E-mailaddress:vidya.2kus@gmail.com(V.K.Sudarshan).

approximately246,660newcasesofinvasivebreastcancerwillbe diagnosedamongwomen[23].

Breastcanceristheresultofuncontrolledmultiplicationofcan- cercells in thebreast and commonlyit begins at thelobes of mammaryglands[1].Thisgroupofrapidlydividingcancercells inthebreastmayeventuallyformalumpknownasbreasttumor.

Breastlesioncanbebenign(notcancerous)ormalignant(cancer- ous)dependingontheircharacteristicsandthedegreeofriskthey carry[1].Imagingofbreast lesionsisnowregardedasessential inadditiontotheclinicaldiagnosisofbreastmalignancies[122].

Mammographyandultrasound(US)remainasthestandardfront linetechniquesforbothscreeningandsymptomaticdiseaseeval- uation.However,mammographyperformedonthedensebreast mayfrequentlyproducefalse-negativeresults[22,68,69,101]and

http://dx.doi.org/10.1016/j.bspc.2016.11.004 1746-8094/©2016ElsevierLtd.Allrightsreserved.

thusdelaythediagnosisofcancer.Fewstudieshavedemonstrated thataddinganUSscantoscreenwomenhavingthedensebreasttis- suetogetherwithamammographycandetectadditionalcancers per1000women[20,55].EventhoughtheUSishighlysensitive [106,126]when appliedtodensebreast, lackspecificity [19,31]

resultinginanalarming numberof false-positive;thusincreas- ingtherateofunnecessarybiopsies[49,101].Reportsuggeststhat, usingconventionalUS,itisdifficulttopreciselydistinguishisoe- choiclesionsfromthesurroundingfat[89].Therefore,despiteof havingregularscreeningtests,thebreastcancersfailtobediag- nosedatanearlycurablestage.

Clinically,it is essential to evaluatethe lesion location sur- roundingtissue and thelesion characteristics suchas size and shapeaccordingtotheBreastImaging-ReportingandDataSystem (BI-RADS)criteriausingeithermammographyoranUSforchar- acterizationofmalignancies [87].Theprimarycharacteristicsof breastlesionsincludetheclarityandcontourofthelesionmar- gins, theorientationand shape of thelesion, boundaryechoes, theecho texture and echogenicity [17,61,106,109]. In addition, the lesion compressibility and vascularity may also be evalu- ated.Benignbreastlesionsappearround,ovalminimallylobulated inshape withintenseboundaryechoes andhomogenous inter- nalechoes[8,18,106].Whilethemalignantbreastlesionsappear withoutpropermarginorboundary,andmayexhibitheteroge- neousechopatterns,andanincreasedanteroposteriordimension [106].BreastUScanbeusedtocharacterizethesephysiological andpathologicalcharacteristicsofbothpalpableandnonpalpable breastlesions[18,106].Despitethis,itlacksfundamentalandquan- titativeinformationonthetissueelasticpropertiesascancertissue isharder,stiffer(lesselastic)andlesscompressiblecomparedto normalbreasttissue[43,102].Moreover,improvedbreastUSimag- ingmodalitiessuchasUStomography [37,98]and multi-modal UStomography(ultrasoundtransmissiontomography(UTT)and ultrasoundreflectiontomography(URT))[94,125]areintensively becomingeffective inassessingthebreast lesioncharacteristics [94,98].Althoughtheinitialresultsare promising,furtherwork withlargersetofbreastcancerpatientsarerequiredtodemon- stratetheirdiagnosticefficacybeforeclinicalapplication[94,98].

Currently,toconquertheselimitationsandattainmoreprecise lesioncharacterization,breastUSelastography(USE)isintroduced [13,15,35,54,99]. Breast USE technique is used in breast lesion assessmentandcharacterization[60].Numerousworkstestified thatitcanimprovetheB-modeUSspecificityindistinguishingthe malignantandbenignbreastlesions[27,28,60].Itisusedtoassess thetissuedeformity(elasticity)andquantifythestiffnessofthe tissue[60,93,103].

AmongthetwoavailableUSEtechniquessuchasstrainelastog- raphy(SE)andshearwaveelastography(SWE)[116],SWEisthe onlyonethatishighlyreproducible[32,40,50,88].InSWE,trans- verselyalignedshearwavesareproducedbyanacousticradiation forceduringtheapplicationofanultrasoundprobetothetissue [77].Thewavestravelfasterinstiff(hard)tissuesthaninsofttis- sues[49].Foreverypixelintheregionofinterest(ROI),itprovides animagewithcolor-codepresentingtheshearwaveelasticity(kilo- pascals,kPa)orvelocity(m/sec)[115].Commonly,acolorscaleof0 (darkblue)isusedforthesoftbreastlesionsand+180kPa(red)for hardlesions[77].Inadditiontothequalitativeparameters(lesion andanadjacenttissuestiffness,lesionsize,shape,andrimstiff- ness),quantitativeparametersofthelesionsuchasmeanelasticity (Emean),maximumandminimumelasticity(Emax,Emin)andelas- ticityratio(Eratio)canbeassessedusingtheSWEandareusedin classificationofbenignandmalignantbreastlesions[10,67].

According to a study, elasticity parameter measured in the breast lesions (benign lesions <80kPa and malignant lesions

>100kPa) can bea good indicatorfor differentiation of benign andmalignantlesions[108].Variouscutoffvaluesfortheelastic-

ityparameterssuchas80kPa[24],30kPa[78]and65kPa[21]are proposedbydifferentstudies[21,25,39].Olgunetal.[91]assessed theminimum,meanandmaximumelasticityvalueswithdifferent cutoffindifferentiatingthemalignantfrombenignlesionsusinga SWE.Thestudyresultsshowedthesensitivityandspecificityof(i) 96%and95%forthemeanelasticitywithacutoffvalueof45.7kPa, (ii)95%and94%forthemaximumelasticitywithacutoffvalue of54.3kPa,and(iii)96%and95%fortheminimumelasticitywith acutoffvalueof37.1kParespectively.Ingeneral,thevelocityis higherforthestiffertissueinorderforthewavestotravelthrough them.Thesimplelumpsproduce0velocity,becauseofthepresence ofnon-viscousfluidsinwhichtheshearwavesdonottravel[16].

Thus,usingtheSWE,thedegreeoftissuedeformationisevaluated wherethestiffertissuesinfiltrated withcancerdeformlessand stiffer(lesselastic),therebycanbeeasilydifferentiatedfromthe normalandbenignsurroundingtissues[71].Variousresearchstud- iesareconductedforevaluatingthesequantitativeSWEparameters inordertodifferentiatethemalignantbreastlesionsfrombenign breastmasses[21,25,40,52,66,75,76,110,114,29].Table1summa- rizesafewofthestudiesonbreastlesionidentificationusingthe SWEimageparameters.Inmostofthestudies,theSWEparameters aresignificantlyhigherformalignantbreastlesionsthanthebenign cases[21,25,39,76,110,123].Themeanstiffnesselasticityhasbeen foundtobeausefulparameterinyieldinganaccuratebenignand malignantdifferentiationofsolidmasses[40].Inaddition,forinva- sivebreastcancers,itisshownthatthebreastlesionsarestiffer comparedtothenormallesions[41]andoftenproduceareasof stiffnessthatarelargerthanthegray-scaleabnormalitygenerated byB-modeultrasound[60].

However,oftenduringthebreastSWEassessments,difficulties arefacedininferringthesignificanceofelasticityvalues,duetothe variousscanninganglesprovidingdifferentelasticityvalues.The reasonforthesedifficultiesisthatbreastlesionsareheterogeneous andthreedimensionalstructures[44].Toresolvethisissue,Kim etal.[64]conductedastudyforevaluatingtheeffectofscanning angleonthediagnosticperformanceoftheSWEindiscriminating thebreastmalignancyfrombenignlesions.Resultsproposethatthe useoftwoorthogonalviewsthatcapturetheimageswillincrease thediagnosticperformanceofbreastSWE.

Many recent studies suggest that the SWE improved the diagnostic performance accuracy and specificity of conven- tional US alone in the diagnosis of breast lesions [21], [26,40,75,76,82,110,113,124]. By adding SWE, about 90% of 4a massesaredowngradedtoBI-RADScategory3,thus,unnecessary biopsiesonthebenignlesionscanbereduced[12,25,39,80,117].

However,itisobservedthatthesmallbreastcancersarenotasstiff asthelargercancers,indicatingthatthetumorsizeaswellasthe specifichistologicaltypecanalsoaffectthestiffnessvalue[44,76].

Incontrast,ithasbeenreportedthatthediagnosticperformanceof elastographyisbetterthantheconventionalUSinthecharacter- izationofsmallmasses(1cm)[86].Therefore,in2015,Kimetal.

[65]evaluatedthediagnosticperformanceofSWEfeaturescom- binedwithanUSintheassessmentofsmall(≤2cm)lesions.The studyreportedthatbycombiningthetwotechniques,specificity increasedandthenumberofunnecessarybiopsiescanbereduced whileevaluatingthesmallbreastlesions.Itisalsoshownthatthe breast lesionstiffness quantitatively measuredbytheSWEis a helpfulpredictorofunder-estimatedmalignancyinanUS-guided 14-gauge coreneedle biopsy(CNB)[95].Leeetal. [79]claimed thattheSWEishighlysensitiveinanaccurateidentificationofthe presenceofresidualbreastmalignantlesionsevenafteraneoad- juvantchemotherapy(NAC)andshowedanimproveddiagnostic performance(sensitivity83.6%andspecificity80%)comparedto theB-modeUS(sensitivity72.1%andspecificity50%).

Recentlyin2016,Ngetal.[90]investigatedtheefficiencyof theSWEinclassifyingbenignandmalignantusing159SWEbreast

Table1

SummaryofstudiesonbreastlesionidentificationusingtheSWEimageparameters.

Author(year) Data Methods/Features Classification Findings/Results

Evansetal.[40] UltrasoundwithSWE Subjects:52patients(23 benignand30malignant lesions).

Emean StatisticalAnalysis Accuracy=91%

Sensitivity=97%

Specificity=83%

Changetal.

[25]

UltrasoundandSWE Subjects:158womenwith182 breastlesions(89malignant, 93benign)

Emean StatisticalAnalysis Cutoff80.17kPa:

Accuracy=86.8%

Sensitivity=88.8%

Specificity=84.9%

Evansetal.[39] BI-RADSultrasoundandSWE Subjects:173womenwith175 breastlesions(64benignand 111malignant)

Emean,Emax StatisticalAnalysis SWEalone:

Accuracy=89%

Sensitivity=95%

Specificity=77%

SWEandBI-RADSUScombined:

Accuracy=86%

Sensitivity=100%

Specificity=61%

Changetal.

[24]

Ultrasound,SWEandstrain Elastography

150breastlesions(71 malignant,79benign)

EmeanandSD Statisticalanalysis

andROC

Ultrasound:

Sensitivity=100%

Specificity=19%

SWE:

Sensitivity=95.8%

Specificity=84.8%

StrainElastography:

Sensitivity=81.7%

Specificity=93.7%

Youketal.[52] UltrasoundandSWE Subjects:389breastmasses (269benignand120 malignant)

SWEfeatures:

Eratio,Emin,Emax,Emean USfeatures:

Diameteranddepthoflesion, distancefromthenipple.

Chi-auqareand Mann-WhitneyU test

Eration(5.14cutoff):

Sensitivity=88%

Specificity=90.6%

Leeetal.[76] Ultrasoundand2Dand3DSWE Subjects:134womenwith144 breastlesions(67malignant)

Emax,Emean,Eratio ROCstatistical analysis

2Dand3DSWE=improved specificityofultrasoundfrom 29.9%to71.4%.

Youketal.

[115]

UltrasoundwithSWE Subjects:123patientswith 130breastlesions(49 malignantand81benign)

Emean,Emax,SDandwSD(SD ofelasticityofthewholebreast lesion)measuredinkPaand m/sec

AUCofROC AUCforthewSD=0.964kPaand 0.960m/sec

SpecificityofSDusingkPaand m/sec=95.1%vs87.7%

Youketal.

[116]

Ultrasound,strain elastographyandSWE Subjects:79breastlesions

Qualitativeandquantitative (SWEparameters)parameters

Statisticalanalysis SWE:

56%ofcategory4alesionswere downgraded.

Leeetal.[78] UltrasoundandSWE Developmentcohort:159 breastmasses(21malignant) Validationcohort:207breast masses(12malignant)

Emax t-test Reportedincreaseinspecificity.

Olgunetal.

[91]

UltrasoundandSWE Subjects:109patientswith 115lesions(83benignand32 malignant)

Emin,Emean,Emax,mass/fat Eratio

StatisticalAnalysis -SPSS

Emean(45.7kPacutoff):

sensitivity=96%,specificity=95%

Emax(54.3kPacutoff):

sensitivity=95%, Specificity=94%

Emin(37.1kPacutoff):

Sensitivity=96%

Specificity=95%

Mass/fatEratio:

Sensitivity=97%

Specificity=95%

Auetal.[12] UltrasoundandSWE Subjects:112womenwith123 masses(79benignand44 malignant)

Emean,Emax,Eratio StatisticalAnalysis -SPSS

Eratio(3.56cutoff)

CombinedultrasoundandSWE:

Accuracy=90.24%

Specificity=87.34%

ByaddingSWEparametrsto BI-RADScategory4amasses,about 90%ofthemcouldbecorrectly downgradedtocategory3,thereby avoidingbiopsy.

Kimetal.[65] UltrasoundwithSWE Subjects:171patientswith 177smallbreastlesions(22 malignantand155benign)

SWEparameters–Emean, Emax,Eratio

Statisticalanalysis –SPSS

Emax(cutoff87.5kPa) Sensitivity=68.2%

Specificity=87.1%

Table1(Continued)

Author(year) Data Methods/Features Classification Findings/Results

Leeetal.[79] UltrasoundwithSWEandMRI data

Subjects:71patientswith breastcancers

Emax ROC Ultrasoundalone:

Accuracy=69%

Sensitivity=72.1%

Specificity=50%

SWEalone:

Accuracy=83.1%

Sensitivity=83.6%

Specificity=80%

UltrasoundandSWE:

ROC=significantlyhighthanthat ofultrasoundalone

Leeetal.[80] UltrasoundandSWE Subjects:139patientswith140 breastlesions(30malignant)

SWEparameters StatisticalAnalysis Emax(cutoff108.5kPa) Sensitivity=86.7%

Specificity=97.3%

Xian-Quan etal.[113]

UltrasoundandSWE Subjects:302breastlesions

Emax,Emean,Emin,Esd,Eratio StatisticalAnalysis SWE:Emax:

Sensitivity=87%

Specificity=97%

Ngetal.[90] UltrasoundwithSWE Subjects:159breastlesions(85 benign,74malignant)

SWEparameters–Maximum elasticity(Emax),Mean elasticity(Emean),minimum elasticity(Emin),Ratioof lesionelasticitytosurrounding tissue(Eratio),SD

Statisticalanalysis –SPSS

Sensitivity=100%

Specificity=97.6%usingEmax parameterindetectingmalignant lesions

Choietal.[29] UltrasoundandSWE Subjects:81non-masslesions (74malignantand7benign)

SWE:

Emax,Emeanandmaximum stiffnesscolor

US:Lesionsize

t-test Chi-squaretest Fisher’sexacttest

SWE:

Emean(85.1kPacutoff) Accuracy=84.5%

Sensitivity=78.4%

Specificity=95.2%

Emax(92.5kPa) Accuracy=83.6%

Sensitivity=78.4%

Specificity=92.9%

US+SWE:

Emean(85.1kPaorhigh vascularity):

Accuracy=84.5%

Sensitivity=95.9%

Specificity=64.3%

Lietal.[82] UltrasoundandSWE Subjects:276patientswith 296breastlesions(212benign and84malignant)

SWvelocity StatisticalAnalysis SWEalone:

Sensitivity=67.9%

Specificity=86.3%

Computer-AidedDiagnosticSystem Loetal.[83] USandSWE

Subjects:57benignand31 malignanttumors

18SWEfeatures(mean, variance,skewness,kurtosis, density,averagefromRGB colorpatterns)

Logisticregression classifier

SWE:

Accuracy=81%

Sensitivity=61%

Specificity91%

Zhangetal.

[121]

UltrasoundandSWE Subjects:125womenwith161 breasttumors

Contourlet-basedtexture features(Tmean,Tamx, Tmedian,Tqt–thirdquartile, Tsd–standarddeviationofthe subbands)–firstorder statistics

ROCandFisher classifier

Accuracy=92.5%

Sensitivity=89.1%

Specificity=94.3%

lesionsimages. Theexperimentalresultspresented 100%sensi- tivityand97.6%specificitywithacutoffvalueof≥56kPaforthe maximumstiffnessindetectingmalignantlesions.Inaddition,their experimentclaimedthat,ifthemaximumstiffnesscutoffvalueis kept≥80kPa,95.5%ofBI-RADS4alesionscanbedowngradedtoBI- RADS3,therebynegatingtheneedforthebiopsy.Eventhough,the SWEtechniqueisclassicallylessoperator-dependent,somedegree ofvariabilitymayoccuriftoomuchpressureisappliedtotheprobe [13].Thus,thereliabilityoftheSWEmethoddependsontheoper- ator’strainingandexperience,ifneglected,technicalerrorssuch asprobemovementorcompressioncancauseinaccurateresults [32,52,88].Moreover,manualevaluationoftheSWEparameters istime-consumingandpronetoerrors[13].Therefore,inorderto overcomethesemanualortechnicalerrors,thecomputer-aided methodsarerequiredforthebenignandmalignantbreast SWE imagecharacterization.

In2015,Lo etal.[83],proposeda computer-aideddiagnosis (CAD)tooltoassessthebreastlesionsusingSWEimages.Firstorder

statisticsbasedfeaturessuchasmean,variance,kurtosis,skewness anddensityofthedifferentcolorchannels(Red,Green,andBlue− RGB)areextractedfromthetumorROIsoftheSWE.Furthermore, thesefeaturesfromthe3colorchannelsaremergedasavectorto assessthetissueelasticity.ThestudyreportedthattheSWEparam- etersshowedanaccuracyof81%inclassificationofBI-RADS2,3, 4,5breastlesionsand83%inclassificationofonlyBI-RADS4cate- gorybreastlesionsusingalogisticregressionclassifier.Zhangetal.

[121]assessedtheelasticheterogeneityofbreasttumorsin the SWEusingcontourlet-basedtextureanalysis.Theyextractedthe firstorderstatisticalfeatures(Tmean,Tmaximum,Tmedian,Tthird quartileandTstandarddeviation)fromthedirectionalsubbands afterthecontourlettransform.Theirstudyreported92.5%accuracy, 89.1%sensitivityand94.3%specificityintheclassificationofbenign andmalignantbreasttumors.However,firstorderstatisticalfea- turescannotcapturethehigher–orderinterrelationshipspresentin theimages.Therefore,thereisaneedforsecond-orderorhigher-

orderand/ornonlinearfeatureextractionmethodstocapturethe subtlechangesoccurringwithintheimages[85].

It is evident from the literature review (Table 1) that few researchers [83,121] have proposed an automated benign and malignantbreastlesionclassificationsystemusingtheSWEimages.

Inviewofthis,inthiswork,auniquealgorithmforanautomated characterizationofthebenignandmalignantbreastlesionsusing SWEimagesis proposed.Fig.1 showstheblockdiagramofour proposedalgorithm. Noveltyof ourproposedsystemis thefor- mulationofaShearWaveBreastCancerRiskIndex(sBCRI)for theclassificationofbenignandmalignantbreastlesions.Initially, theDiscreteWaveletTransform(DWT)isperformedontheSWE imagesuptothreelevelstoobtaindifferentcoefficients.Fromthese DWTcoefficients,varioussecondorderstatisticsusingRunLength Statistics(RLS)andHu’smomentsareextractedandsubjectedto sequentialforwardselection(SFS)method.Thesignificantfeatures obtainedarerankedusingReliefFalgorithm.Alltherankedfea- turesaresubjectedtodecisiontree(DT),Knearestneighbor(KNN), lineardiscriminantanalysis(LDA),quadraticdiscriminantanalysis (QDA),supportvectormachine(SVM),andprobabilisticneuralnet- work(PNN)classifiersfortheautomatedcharacterizationofbenign andmalignant.Theperformanceoftheseclassifiersaretestedusing 10-foldcrossvalidationmethod.

2. Datacollection

ThedataforthecurrentstudywereobtainedfromtheDepart- mentofBiomedicalImaging,UniversityofMalayaMedicalCentre (UMMC), Malaysia. The necessary institutional medical ethics committee board approval was obtained for this study. The patientswererecruitedfromJune2012toApril2013byobtain- ingtheinformedconsentbeforetheirrecruitment.Allthepatients recruited werehavingbreast lesionsof BIRADS4 category and above,and wereeven scheduled for ultrasound corebiopsy. A total of 156patients (73 with malignant and 83 with benign) were recruited with either palpable lumps or sonographycally detectedlesions.Allscans were performedusing theAixplorer ultrasoundsystem(SuperSonicImagine,AixenProvence,France) usinga15–4MHzlineartransducerprobe.Initially,B-modegray scale images were captured using ultrasound alone and about 3–5min later, elastography images were generated using SWE method[90].

3. Methodology 3.1. Pre-processing

Theimagesarecropped(about195×195resolution)tohave onlytheSWE3×3cmcolormapinthetransverseplaneplaced ontheregionencompassingthelesionsanditsimmediate sur- roundingtissues.Later,thered-green-blue(RGB)colorSWEimages areconvertedintograyscaleandthecontrastoftheseimagesare enhancedbyusingadaptivehistogramequalization[51].

3.2. Featureextraction

Thisstageisessentialfortheinterpretationofabnormaland normalclasses.ThesecondorderstatisticsRLSandHu’smoments techniques are implemented in this work to analyzethe SWE images.VariousRLSandHu’smomentsfeaturesareevaluatedfrom theDWTcoefficientsofSWEbreastimages.Thedetailedproposed methodologyisexplainedinthissection.

3.2.1. Discretewavelettransform(DWT)

Thistechniqueconvertsthetimedomainsignalintowavelet domaintoobtainthetimeandfrequencyvaluesintermsofcoef- ficients[9,92].Filterbanksareusedtodecomposetheimagesinto high-pass(detail)andlow-pass(approximation)components[84].

Bydecomposingtheoriginalimageusinghigh-andlow-pass filters,foursubbandimagesLL,LH,HL,andHHareobtained.The LLcoefficientssignifythetotalenergyinanimageandarecalled approximationcoefficients.ThedetailcoefficientsHL,LH,andHH arevertical,horizontal,anddiagonaldetailsoftheimagerespec- tively.Theapproximationcoefficientsarefurtherdecomposedto obtainthesecond-levelsubbandimage.Theseindividualsubbands representedasHDCL_i,VDCL_i,DDCL_i,andADCL_iarethehorizontal, vertical,diagonalandapproximationcoefficientsacquiredatthe i^thleveldecompositionrespectively[34].Inthiswork,DWTisper- formedontheSWEofbenignandmalignantimagesuptothree levelsusingbiorthogonal3.1 (bior3.1)mother waveletfunction [2].Fig.2showsthethreelevelDWTcoefficientsof benignand malignantSWEimages.

ItcanbeseenfromFig.2that,thereisdistinctvariationinthe pixelsofbenignandmalignantDWTsubbands.Thesubbandsof malignantlesionexhibitmoresuddenvariationsascomparedto benignmaybeduetothesuddenchangesinthepixels.Wehave extracteddifferentfeaturesfromtheseDWTcoefficientsofvarious subbands.

3.2.2Gylevelrun-lengthmatrix(GLRLM)orrun-lengthstatistics (RLS)

Gallowayetal.[42]introducedtheideaofRLStocapturethe detailsofanimagefromitsgraylevelruns.Setofimagepoints withsimilargraylevelsarerepresentedbygraylevelrun.Inthat run,thecountofimagepointsisalengthoftherun.Arun-length matrixp(i,j) storesinformationofthecountofrunswithpixels ofgrayleveliandrunlengthj.WithMrepresentingthenumber ofgraylevels,Nthenumberofruns,Nrthenumberofdifferentrun lengths,andNgthenumberofgraylevelsandPthenumberofpixels intheimagerespectively,theShortrunsemphasis(sR^x_E),Graylevel non-uniformity(GL^x_−u),Longrunsemphasis(lR^x_E),Runlengthnon- uniformity(RL₋^x_u)andRunpercentage(R^x_P)featuresareextracted usingRLSmethods.

Chuetal.[30]proposedanewtechniquebasedonthegrayvalue distributionoftherunsand definedtwo differentfeaturesLow gray-levelrunemphasis(lGLR^x_E)andHighgray-levelrunemphasis (hGLR^x_E).Dasarathyetal.[33]proposedanewmethodfortheimage characterizationbasedontheideaofjointgraylevelandrunlength distributions.ThefourfeaturesShortrunlowgray-levelemphasis (sRlGL_E^x),Shortrunhighgray-levelemphasis(sRhGL^x_E),Longrunhigh gray-levelemphasis(lRhGL^x_E)andLongrunlowgray-levelemphasis (lRlGL^x_E)proposedbythismethodareextracted.

3.2.3. Hu’smoments(Hu^x_m)

In this work,seven moments (Hu^x_m, where m=1,2,3,....7) developed by Hu which are invariant to rotation, scaling and translationvariationsoftheimagesareextracted[3],[57].These momentscapturethedetailsaboutintensitydistribution,geomet- rical(shape)featuresandhelpstoidentifythepatternsinimages [57],[120].

3.3. Featureselectionandranking

Allnonlinearfeaturesassessedarenotcapableofclassifyingthe benignand malignantgroups. Thus,torecognize theextremely resourcefulfeatureswithusefulinformation,sequentialforward featureselectionmethodisused.TheSequentialforwardselection (SFS)selectsthefeaturessequentiallyandaddstoanemptysub setuntiltheadditionoffeaturesmaximizestheclassificationaccu-

Fig.1.Blockdiagramofproposedalgorithm.

Fig.2.Three-levelsub-bandsof2DDWT(bottomrow)ofSWEimages(toprow):(a)benignand(b)malignant.

racy[70],[81].ThemainadvantagesofusingthisSFSmethodisits computationalefficiencyandavoidsoverfit[72].Inaddition,this SFSmethodoffeatureselectionhasfrequentlyshowntoperform competitivelycomparedtoothermethodlikesequentialfloating forwardselection(SFFS)[96],[97].Thus,thismethodisconsidered asoneofthestate-of-the-artfeatureselectiontechniques.Selected featuresaresubjectedtoReliefFfeaturerankingmethodwhichuses theknearestneighbors−hits(fromthesameclass)andmisses (fromthedifferentclass).Theaveragesoftheircontributionsare addedtoRelief’sestimate[100].

3.4. Classification

Classificationisperformedontherankedfeaturesusingdiffer- entclassifiersinordertoattain themaximumoutcome.In this work,DT,LDA,QDA,SVM Polynomial1,2, 3andRBF,PNN and KNNclassifiersareexperimentedtofindthebestclassifierindistin- guishingthebenignandmalignantSWEimages[36].DTclassifier, builds a tree fromthe features during thetraining phase [73].

Basedontherulesobtainedfromthetreeitbuilt,classifiesthetwo classesandtherebyidentifiestheclassoftest(unknown)data.KNN classifierdetermines k-nearestneighbors i.e.theminimumdis- tancebetweentrainingandtestingdata[74].Theunknownsample getsthemostcommonclassamongthek-nearestneighbors.SVM classifierinthehigher-dimensionalspaceconstructsaseparating hyperplanewhichsplitsthetrainingsetsintotwoclasses[38].In

thiswork,wehaveusedpolynomial1,2,3andRBFkernelfunc- tions.PNNclassifierworksontheprincipleofsupervisedlearning algorithmandcalculatestheweights.Usingamulti-layeredneural networkhaving4layers,thisclassifierisoftenemployedinclassi- ficationoftwogroups[111].Twotypesofdiscriminantclassifiers suchasLDAandQDAareemployedinthiswork.LDAandQDA learnsfromlinearandquadraticboundariesrespectively[59],[63].

A10-foldcrossvalidationtechniqueisemployedinourworkfor performancevalidationoftheclassifiers.

4. Results

4.1. Resultsoffeatureextraction

Theresultsoffeatureextractionandclassificationarepresented inthissection.EachSWEimageissubjectedtothreelevelDWTto obtainatotalof12DWTcoefficients.Totalof216(12DWTcoef- ficientsX(11RLS+7Hu’smoments))featuresarecomputed.All together216×156featuresarecomputedfrombothbenignand malignantbreastlesions.

Table2 shows themeanand standard deviation(SD)values of second order (RLS) and nonlinear (Hu’s moments) features extractedfromtheDWTcoefficientsofbenignandmalignantSWE images.SignificantfeaturesselectedusingSFSmethodareranked byReliefFtechniqueandthebest3highlyrankedfeaturesaretab- ulatedinTable2andFig.3.

Table2

Results(mean±SD)oftopthreefeaturesextractedfrombenignandmalignantbreastSWEimagesusingRLSandHu’smomentsmethods.

Features Benign Malignant p-Value Criterion

Mean SD Mean SD

RL^A1_−u 2.10E+03 1.78E+02 1.59E+03 2.35E+02 0.0000 0.0229

RL^H1_−u 3.57E+02 1.16E+02 4.15E+02 1.46E+02 0.0067 0.0083

Hu^H1₅ 6.81E+01 2.16E+01 8.08E+01 3.03E+01 0.0028 0.0048

Fig.3.Barplotsofmeanvaluesofextractedfeaturesfrombenignandmalignant SWEimagesusingRLSandHu’smomentsmethod.

ItisevidentfromTable2andFig.3thatthefeaturevaluesare higherinmalignantlesionscomparedtothebenignlesions.This ismaybeduetothemoreabruptchangesinthepixelsofmalig- nantlesion.Inbenignlesion,thepixelvariationsarelessabruptas comparedtomalignantcontributingtothesmallervalues.

4.2. Resultsofclassification

Rankedfeaturesarefedtodifferentclassifiersnamely,DT,LDA, QDA,SVM,KNNandPNNtoachievethehighestclassificationper- formance. QDA classifier has achieved the maximum accuracy, sensitivityand specificityof 93.59%,90.41% and 96.39% respec- tivelyusingtheDWTmethodwiththreefeatures(RL₋^A1_u,RL^H1_−u,Hu^H1₅ ).

Table3showstheresultsofclassificationobtainedusingsecond- order statistics, RLS and Hu’s moments. It is evident from this Table3thattheQDAoutperformedotherclassifiersinachieving thehighestresults.

WehavealsoperformedclassificationwithouttheDWTusing onlyRLSandHu’smomentsdirectlyfromtheSWEimages.Wehave achieved88.46%accuracy,83.56%sensitivityand92.77%specificity usingonlytwofeatures.Therefore,extractingfeatures(RLSandHu’s moments)fromtheDWTcoefficientshassignificantlyimproved theclassificationperformance. Thus, fusion ofthree techniques DWT,RLSandHu’smomentsshowedbetterresultsinanefficient characterizationofthebenignandmalignantbreastlesionsfrom SWEimages.

Fig.4.PlotofsBCRIforbenignandmalignantbreastlesions.

4.3. Resultsofindex−sBCRI

Apartfromtheclassifiers,anideaofintegratedindex,firstintro- ducedbyGhista[45],[46,47],isproposedforthecharacterization ofbenignandmalignantbreastlesionsinthiswork.Anintegrated indexnamedsBCRIisformulatedbymergingthemostdistinctive featuresinsuchawaythattheindexvalueisnoticeablydifferent forthetwoclasses.

Inthecurrentwork,asBCRIisformulatedusingthetwofea- turessuchasRL^A1_−uandHu^H1₅ obtainedfromtheSWEimages(Table2).

Thesetwofeaturesarethencombined(seeEq.(1))toproducean indexwhichishighlydistinguishing.Mathematicalequationofthis sBCRIisgivenas,

sBCRI=

1.5×

RL^A1_−u

+2.2×

Hu^H1₅

+5.5

1000 (1)

ThissBCRIformulatedgivesamaximumdistinctionbetweenthe twoclasses(benignandmalignant).Table4showsthesBCRIvalues forthesubjectswithmalignantandbenignbreastlesions.Aplotof sBCRIforthebenignandmalignantcasesobtainedusingthecom- binationoftwofeatures(RL^A1_−uandHu^H1₅ )isshowninFig.4.Thus, sBCRI(asinglenumber)aidsthecliniciansinfasterandanaccurate characterizationofthebenignandmalignantbreastlesions.

5. Discussion

Inthiswork,auniquealgorithmisproposedbyfusingDWT,tex- tureandHu’smomentsfeatures.DWTmethoddecomposesSWE

Table3

ResultsofclassificationofbenignandmalignantSWEimagefeaturesobtainedusingRLSandHu’smomentsmethods.

Classifiers NOF TP TN FP FN Acc(%) Sen(%) Spe(%)

DT 3 62 76 7 11 88.46 84.93 91.57

LDA 3 64 81 2 9 92.95 87.67 97.59

QDA 3 66 80 3 7 93.59 90.41 96.39

SVMPoly1 3 64 81 2 9 92.95 87.67 97.59

SVMPoly2 3 62 80 3 11 91.03 84.93 96.39

SVMPoly3 3 66 78 5 7 92.31 90.41 93.98

KNN−25 3 63 82 1 10 92.95 86.30 98.80

PNN−0.15 2 61 82 1 12 91.67 83.56 98.80

SVMRBF−1.4 3 66 79 4 7 92.95 90.41 95.18

Acc=accuracy;Sen=sensitivity;Spe=specificity;TP=truepositive;TN=truenegative;FP=falsepositive;FN=falsenegative;NOF=numberoffeatures.

Table4

RangeofasBCRIforthebenignandmalignantbreastlesions.

Benign Malignant

Mean SD Mean SD

3.306607 0.27379 2.570718 0.367312

image intothe sets of images atdifferent positions and scales by using filter banks.Hence this time-invariant decomposition method(DWT)isveryeffectiveinseparatingnoisefromanimage [62,107].Animportantbenefitofthismethodisitsabilitytotrea- suretemporalresolutionoftheimages;inotherwords,itcaptures bothfrequencyandlocationdetails[6,84].Furthermore,DWTtech- niqueisrobustandusefulindetectingirregularitiespresentinthe images[4,6,7].Thus, DWTmultiresolutionanalysistechnique is advantageousforobtaininganefficientperformanceusingtheSWE images.

SeveralotherresearchershaveanalyzedtheSWEimagesusing quantitative parameters [21,25,40,64,66,76,90,110], texture fea- tures [83,121] in thecharacterization of benign and malignant breastlesions.Manualevaluationofbreastlesionparametersusing SWEispronetoerrorsduringdataacquisitionandvisualinterpreta- tionvariabilitiespresentindifferentUSEequipmentmanufacturers [13,104].Anelasticityvaluesometimescannotbecalculatedwhen thedeformationoftissueistoolow.Thismayoccurinlargevery rigidinfiltrativecancers[110].Thesystemscannotmeasurethe elasticityvaluesinlesionswheretissuedoesnotvibrateenoughor theamplitudeofshearwaveistoolowandthuslostintheback- groundnoise[14].Chancesofhavingmorefalsepositiveishighfor largesizelesionandthickbreast,andmorefalse-negativevalues forthesmalleranddeeperlesions[114].

Thus, analysis of the image texture characteristics using computer-aided technique may overcome these limitations of manualevaluationofSWEparameters.Moreover,thehighqual- itySWEimageshaveshownanimproveddiagnosticperformance incharacterizingthemalignantbreastlesionsfrombenignones [25,76].Hence,thereisaneedforanefficientimagepre-processing techniquebeforeextractionofthefeatures.

Hence,tosurpasstheobstacles,anoveltechniqueforanauto- matedcharacterizationofthebenignandmalignantbreastlesions fromSWEimagesis proposedin this work.DWT isperformed onthebenignandmalignantSWEimages.VariousRLSandHu’s momentsfeaturesarecomputedfromtheDWTcoefficients.Signif- icantfeaturesareselectedfromtheextractedparametersusingSFS method.AlltheseselectedfeaturesarerankedaccordingtotheReli- efFrankingmethod.Later,therankedfeaturesareclassifiedusing differentclassifiers.Inaddition,anindex calledsBCRIis formu- latedanddevelopedusingthesecond-orderRLSandnonlinearHu’s momentsfeaturesextractedfromtheSWEimagesforcharacteriza- tionofmalignantandbenignbreastlesions.Ourproposedmethod showed93.59%accuracy,90.41%sensitivityand96.39%specificity incharacterizingthebenignandmalignant breastlesionsusing

onlythree(RL^A1_−u,RL^H1_−u,Hu^H1₅ )featuresextractedfromtheDWTcoef- ficients(approximationandhorizontal).Wehavealsoperformed the classification using same features withoutDWT technique.

Resultsshowedlowperformanceinthecharacterizationofmalig- nantandbenignascomparedtotheDWTtechnique.Therefore, fusionofDWTtechniqueexhibitedthehighestperformanceinthe characterizationofmalignantandbenignSWEimages.

Moreover,thesecond-orderand nonlinearfeatures (RLSand Hu’smoments)usedinthisworkarecapableofextractingthespa- tialinterrelationshipsandcomplexitypresentinpixelsofimages [5,118].Secondorderstatisticsevaluatethepropertiesofmorethan onepixelvaluesatspecificpositionsrelativetoeachother[105].

ThesecondorderstatisticscalculatedusingRLSmethodprovides foreachsamplethelargesetoffeatures,thus,evensmallvaria- tions(subtlechanges)occurringinanimagetexturearecaptured bythesefeatures[53,105].Hu’smomentsarewidelyusedinimage patternrecognitionduetoitsinvariantnatureofimagetransla- tion,scalingandrotation[57,58].Moreover,thesefeatures(RLS andHu’smoments)evaluatedfromtheDWTcoefficientsprovide informationrelatedtothedifferentfrequencybandspresentinan image[5,118].Therefore,inessence,thesecond-orderstatisticsand nonlinearfeaturesaresuitableforobtaininginformationfromthe SWEimagesandcanbesuccessfullyusedforthedevelopmentof anefficientdiagnosticsystem.

In comparison to theother publishedstudies,our proposed methodachievedbettersensitivityandspecificityindifferentiat- ingthemalignantandbenignSWEimages.Thisproposedtechnique canassist,ifusedinhospitalsorpolyclinicstodiagnosethemalig- nancyaccuratelyandhenceavoidtheunnecessarybiopsiesasit hashighspecificity.Themainnoveltyofourproposedworkisthe formulationofanintegratedindexsBCRIforthecharacterization ofmalignantbreastlesionsusingonlythetwofeaturesextracted fromSWEimages.Infuturework,wewillbeexploringtodevelop anautomatedsystemusingSWEforthecharacterizationofbenign, pre-malignantandmalignantbreastlesions.

6. Conclusion

Automatedcharacterizationofbreastmalignancyfrombenign lesionsusingSWEisachallengingtask.Inthispaper,anintegrated index,sBCRI,foranautomatedcharacterizationofbreastmalignant lesionsisproposed.DWTtechniqueperformedonSWEimageshas achievedthehighaccuracy,sensitivityandspecificityof93.59%, 90.41%and96.39%respectivelyusingthreefeaturesinthecharac- terizationofmalignantandbenignlesions.Theproposedtechnique isreliableandpromising.Inaddition,oursystemisaffordabletothe clinicianswithouttheneedforanyspecialhardwaresetup.Thus, thissoftwarecanbeincorporatedintheroutineclinicalpracticeas ascreeningtooltodiagnosebreastlesions.