Comparative assessment of texture features

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Comparative assessment of texture features

for the identification of cancer in ultrasound images:

a review

Oliver Faust

^a,

* , U. Rajendra Acharya

^b,c,d

, Kristen M. Meiburger

^e

,

Filippo Molinari

^e

, Joel E.W. Koh

^b

, Chai Hong Yeong

^d

, Pailin Kongmebhol

^f

, Kwan Hoong Ng

^d

aDepartmentofEngineeringandMathematics,ShefﬁeldHallamUniversity,UnitedKingdom

bDepartmentofElectronic&ComputerEngineering,NgeeAnnPolytechnic,Singapore

cDepartmentofBiomedicalEngineering,SchoolofScienceandTechnology,SingaporeUniversityofSocialSciences, Singapore

dDepartmentofBiomedicalImaging,FacultyofMedicine,UniversityofMalaya,KualaLumpur,Malaysia

eDepartmentofElectronicsandTelecommunications,PolitecnicodiTorino,Turin,Italy

fFacultyofMedicine,DepartmentofRadiology,ChiangMaiUniversity,Thailand

article info

Articlehistory:

Received25September2017 Receivedinrevisedform 16November2017 Accepted5January2018 Availableonline17January2018

Keywords:

Cancer Ultrasound Textureanalysis

ComputerAidedDiagnosis

abstract

Inthispaper,wereviewtheuseoftexturefeaturesforcancerdetectioninUltrasound(US) imagesofbreast,prostate,thyroid,ovariesandliverforComputerAidedDiagnosis(CAD) systems.Thispapershowsthattexturefeaturesareavaluabletooltoextractdiagnostically relevantinformationfromUSimages.Thisinformationhelpspractitionerstodiscriminate normalfromabnormaltissues.Adrawbackofsomeclassesoftexturefeaturescomesfrom theirsensitivitytobothchangesinimageresolutionandgrayscalelevels.Theselimitations poseaconsiderablechallengetoCADsystems,becausetheinformationcontentofaspecific texturefeaturedependsontheUSimagingsystemanditssetup.Ourreviewshowsthat singleclassesoftexturefeaturesareinsufficient,ifconsideredalone,tocreaterobustCAD systems,whichcanhelptosolvepracticalproblems,suchascancerscreening.Therefore,we recommendthattheCADsystemdesigninvolvestestingawiderangeoftexturefeatures alongwithfeaturesobtainedwithotherimageprocessingmethods.Havingsuchacompet- itivetestingphasehelpsthedesignertoselectthebestfeaturecombinationforaparticular problem.ThisapproachwillleadtopracticalUSbasedcancerdetectionsystemswhich deliverrealbenefitstopatientsbyimprovingthediagnosisaccuracywhilereducinghealth carecost.

*Correspondingauthorat:DepartmentofEngineeringandMathematics,ShefﬁeldHallamUniversity,UnitedKingdom.

E-mailaddress:oliver.faust@gmail.com(O.Faust).

https://doi.org/10.1016/j.bbe.2018.01.001

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1. Introduction

In2015,heartdiseasesweretheleadingcauseofdeathinthe United Statesand cancer was the secondleading cause of death.Itispredictedthattheorderwillreverseinthefuture[1].

Therefore,cancerisabigandgrowingpublichealthproblem [2].Table1listspublichealthdatafromtheAmericancancer society[3]. Itshows boththeestimated newcases andthe estimated deaths for ovarian, liver, thyroid, breast and prostatecancers.Thesecancerscontribute34.64%ofallthe estimatednewcancercasesandtheyareresponsibleformore than18.48%ofcancerrelateddeaths.Intermsofpublichealth, the problem can be partitioned into cancer prevention, diagnosis and treatment. Cancer prevention is possible, becausehealthylifestylechoiceslowertheriskfordeveloping cancer. Thelink between lifestyle choicesand cancerwas discovered by studies which showed that cancer rates of migrantsmovetowardstheratemeasuredintheindigenous population[4,5].Smoking,consumptionofcaloriedensefood andreproductivebehaviorsarealsoknowntoincreasetherisk ofgettingcancer[4].

Ultrasound(US)isanon-invasive,costeffectiveandsafe¹ medicalimagingmodalitywhichcanbeusedtodetectcancer [6,7]. Achieving a good diagnosis performance with this imaging technology requires an integrate interplay of ﬁne motor skills (to operate the ultrasound transducer) and cognitiveabilitiesforimageinterpretation[8].Hence,practi- tioners require extensive initial training and continuous practice.Acoreproblemofthishumancentricapproachfor diseasediagnosisisthenon-stationarydiagnosisqualityand inter-aswellasintra-operatorvariability[9].Non-stationary referstothefactthattheperformanceofhumanpractitioners variesover time.These variationscanbe positive,such as gaining more experience over time as well as negative triggeredby fatigue andother externalfactors. Overall,the beneﬁcial properties of US technologyoutweigh thesepro- blems. Therefore,avibrant researchcommunityexplores a widerangeofapplicationareasforthisimagingmethodology.

Initially,USwasusedonlyforapplicationareaswheretissue and bone formations led tosharp edges in the US images [10,11]. Unfortunately, a wide range of diseases cannot be diagnosed based on the edges within an US image alone [12,13]. Many of the new application areas target diseases whosesymptomsandsignsarechangesinsofttissues[14].A prominentexampleofthatproblemclassiscancerdiagnosis, because cancer cells are very similar to normal cells.

Differentiatingmalignantfromnormalcellscanbeimproved byinterpretingimagetexture,since itcontainsinformation aboutthescannedtissues[15].Forahumanpractitioner,the changes in image texture, which indicate the presence of cancer,appeartobeminute.Hence,humantextureanalysisis tiresomeanderrorprone.Asaconsequence,ahumancentric approachleadstoalowdiagnosticaccuracy.

ComputerAidedDiagnosis(CAD)canhelptoovercomethe problemsofhumantextureanalysisandtherebyincreasethe diagnosisaccuracy[16,17].Thechallengeforsuchcomputer basedtextureanalysisistwofold.First,weneedtoestablish

mathematicaldeﬁnitionsforrelevantimagetextures.These mathematicaldeﬁnitionsleadtotextureanalysisalgorithms whichcanbeusedinpracticalCADsystems[18].Thesecond problem is to detect the texture changes, which indicate malignanttissues.Itturnsoutthattheseproblemscannotbe solvedapriori;thetextureinterpretationcanonlybedonea posteriori.Inotherwords,itisimpossibletoknowwhattypeof texture analysis algorithm will be sensitive for the subtle differencesbetweennormaland cancercellsinUSimages.

Therefore,empiricalmethodsidentifywhichtexturemethods workwellforaspeciﬁcproblem[19].Asaconsequence,itis necessarytotestawide rangeof algorithmsandselectthe ones which show the best performance on known data.

Another complicating factor, for computer based texture analysis,isthatmostoftheknowntexturealgorithmsdepend ontheimageresolution.Hence,speciﬁctextureresultsarenot transferablebetweendifferentUScapturingmachines.

Texture information can be extracted using various methods.Inordertoselectthebestalgorithm,itisnecessary tohaveagoodunderstandingoftheavailablemethods.The currentreviewprovidesanoverviewoftheavailabletexture algorithmsandtheirapplications.Wereviewtexture-basedUS imageanalysisintheareasofbreast,prostate,liver,ovarian andthyroidcancerdetection.Thisreviewshowsthattexture featuresarevitalforachievingthediagnosticaccuracyneeded forpracticalCADsystems.Furthermore,wegiveanoverview oftexturealgorithms.Duringthereview,wefoundthatonlya few CAD systems are solely based on texture methods.

Researchwork,thatconsidersonlytexturealgorithms,aims to improve the understanding of the relationship between humantissueformationsandUSimages.Robustandtherefore practicalCADsystemsmustbebasedonarangeofdifferent featureextractionmethods,preferablycomingfromdifferent imaging methods. We recognize that texture-based image analysisisausefulandcosteffectiveenhancementofthewell- knownUStechnology.Forapplicationareas,suchasbreast, liver,ovarian,prostateandthyroidcancer,USbasedtexture featuresarevitalforCAD.

Tosupportourposition,ontextureanalysisformedicalUS images, wehave organizedthearticleasfollows.Thenext sectionprovidespathologicalbackgroundonbreast,prostate, liver,ovarianandthyroidcancerandtheirtypicalcharacter- isticsinultrasoundimages.Thematerialsectioncontainsa comprehensivereviewoftexturealgorithmsandweintroduce methods to measure the quality of these algorithms.

Table1–Estimationofthenumberofnewcasesand deathforselectedcancersintheUnitedstates,2015[3].

Cancertype Estimated

newcases

Estimated deaths

Ovarian 21,290 14,180

Liverandintrahepatic bileduct

35,660 24,550

Thyroid 62,450 1950

Breast 234,190 40,730

Prostate 220,800 27,540

Allthecancertypes fromabovetogether

574,390 108,950

Allcancertypes 1,658,370 589,430

1 Itusesnoionizingradiation.

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2. Background

In this section we will give an overview of prostate, ovarian,liver,breastandthyroidcancersandhowUStexture featuresarerelevantfortheidentiﬁcationofeachindividual cancer.

echopatternsandanincreasedanterioposteriordimension.

Fig.1showsaUSimageofnormalfemalebreasttissue.Fig.2 showsabenigncyst.Fig.3showsamalignantcarcinoma.

2.2. Prostatecancer

Theprostateformsapartofthemalereproductivesystemthat secretes analkaline ﬂuidthatconstitutesabout 30%of the

Fig.1–USimageofanormalbreasttissue.

Fig.2–USimageofabenignbreastcystattheretroaerolarregion.

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semen volume [21].Current methods used for screening for prostate cancer include measuring serum prostate-specific antigen (PSA) levels,transrectal ultrasound scanning(TRUS), digitalrectalexam,andbiopsy.Regardingultrasoundimaging, threedimensionalTRUSiscurrentlyoftenusedandabnormality intheUSimagescanbedeterminedbyprostatesthat(1)have capsularirregularityoranill-definedperipheralandtransitional zone,or(2)arefocalhypoechoic,echogenicorisoechoicwith focalcontourbulgedlesionsintheperipheralzone[22].Fig.4 shows a US image of normal prostate tissue. Fig. 5 shows suspiciousprostatetissuewhichiscalcifiedandenlarged.

2.3. Livercancer

Theliverisavitalorganforhumanhealthandwell-beingthat ﬁlterstoxicsubstancesfromtheblood,synthesizesproteins

and produces biochemicals for digestion [23]. Ultrasound imaging is often used to diagnosis liver disease and in detectinglesions.Anormalliverappearsasastructurewith homogeneous texture and average echogenicity, whereas cysts appearas ananechoic region withposterioracoustic enhancement and atypical metastasis presents a ‘‘target’’

appearancewithahyperechoicrimandahypoechoiccenter [24]. Texture features can therefore be very useful in the diagnosis anddiscriminationoflivercancer.Fig.6showsa USimageofnormallivertissue.Figs.7and8showUSimages oflivercystandlivermetastasisrespectively.

2.4. Ovariancancer

Ovariesareapartofthefemalereproductivesystemandare alsoresponsibleforproducingfemalesexhormones,suchas Fig.3–Breastcarcinoma:thelesion(measuring0.517cmT0.557cm)hasamarkedhypoechogenecity,lacksofcircumscribed margins,showsheterogenousechopatternsandanincreasedanterioposteriordimension.

Fig.4–USimageofanormalprostatetissue.

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estrogenandprogesterone[25].Ultrasoundimaging isoften employedtoimagetheovariesandtoassistinthedetection andclassificationofovariancysts,whichisasacfilledwith liquid surrounded by a very thin wall. Ovarian cysts are typicallyclassifiedbasedonsize[26]andtexturefeaturescan alsoenhancesubtletissuechangeswhichindicateamalig- nanttumor.Fig.9showsanUSimageofanormalovary.Fig.10 showsanovarycyst.

2.5. Thyroidcancer

The thyroid gland, which is located in front of the neck, secreteshormonesthatinﬂuenceproteinsynthesisandthe metabolic rate [27]. US imaging is often used to diagnose thyroidnoduleswhichare typicallycharacterizedashypo-, iso-, or hyperechoic [28]. Thyroid nodules are typically heterogeneous with various internal components, demon-

strating how texture analysis can be a powerful tool in identifyingcancerinthyroidUSimages. Figs.11 and 12 showbenignandmalignantthyroidrespectively.

3. Material and methods

Thefactthatcancerisabigandgrowingpublichealthproblem createsaneedtoinvestigatecosteffectivediagnosismethods.

ThissectionshowshowUSbasedCADforsofttissuecancer canhelptoaddressthisimportant problem.Costefficiency comes from the fact that both US imaging and digital processingareinexpensiveprocesses.Thetaskofthedesigner is tofind suitable image processing algorithms,which can extractgoodqualityfeaturesfromUSimages.Thesefeature extraction algorithms feed their results to classification algorithmswhichprovidediagnosissupport.

Fig.5–USimageofanenlargedprostatewithcalcification.

Fig.6–USimagesofanormallivertissue.Top:shearwaveimage.Bottom:B-modeultrasoundoftheliver.

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Fig.13 showsanoverview diagramfor thedesign of US basedCADsystems.Thedatasetof knownorclassifiedUS imagesiscrucialforthevalidityofthedesignprocess.Tobe specific,onlyifthedataset,withwhichtheCADsystemwas developed,issimilartothemeasurementsobtainedwhenthe systemisdeployedthentheperformancemeasures,acquired duringthedesignprocess,arevalid.Thesubsequentproces- sing and classification methodsare used to find the most suitable algorithm structure. This process is governed by empiricalscienceandsteeredbyperformancemeasures.The nextsectionintroducesanexamplestudyontexture-based featureextractionfromthyroidUSimages.

3.1. Ultrasoundtexture

USimages show ‘‘speckle’’texture, whichresultsfrom the interactionofanultrasonicwavewithtissuecomponents[29].

In many cases, parenthetical tissue structures are small comparedtothe periodlengthof thesoundwaves usedby the US transducer. As a consequence, the sound wave is scattered by these tissue structures. The scattered waves interferewithoneanother,becausewithinoneresolutioncell there areseveral reflectorsand thetransducersums upthe received waves in a coherent manner. The interference producesa granulartexture,known asspeckle.Thespeckle pattern is influenced by a large number of parameters, includingreflectordensity,tissuetypeandeventhepathologi- calstateofthetissue[30].Allthescientificworkreviewedinthe nextsectionisbasedontheassumptionthatspeckle,i.e.theUS image texture, contains information about the investigated tissuestructure.Morespecifically,thereviewedworkaimsto differentiatespeckletextureofbenignfrommalignanttissue.

To demonstrate texture feature extraction, we used 60 texture algorithms on US images of the thyroid gland.

Fig.7–USimageofalivercyst.

Fig.8–USimageofalivermetastasis.

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ThealgorithmswereappliedtoUSscansfrom223patientsof theChiangMaiUniversityHospital,Thailand[31].Theprotocol was approved by the ethics committee and the informed consentwaswaivedduetoretrospectivestudy.Theimages show211benignand31malignantthyroidnodules.Figs.14 and 15 show the performance of these algorithms in differentiating benign from malignant tissue. The t-value [32,7] was used as primary performance measure and the ﬁgureslistthefeaturesindescendingorder,i.e.featureswith thehighestt-valuescomeontopofthelist.Beingontopofthe listindicatesthatthefeaturehastheabilitytodiscriminate betweenbenignandmalignantthyroidnodules.Inadditionto the t-value, the ﬁgures also indicate mean and standard deviation scores, for both benign and malignant cases,

of a particular feature. Ideally, we would like a featureto have distinct mean and low standard deviation values for benign and malignant lesions of US images. Long Run Emphasis(LRE)featuresatisﬁesthatrequirementbetterthan alltheothertestedtexturealgorithms.Asaconsequence,the LREt-valuescoreis3.4354,whichisthehighestamongstthe testedfeatureextractionmethods.However,thisresultholds trueforthisparticulardatasetonly.Otherdatasets,takenwith differentimagingequipment,arelikelytoresultinadifferent feature performance. Therefore, testing the feature perfor- mancewithalargeandvarieddataset,isveryimportantfor thedesignofCADsystems.

Fig. 16 shows a treemap [33] of the t-value results, presentedinTableA.7,showninAppendixA.TheGray-Level Fig.9–USimageofanormalovarytissue.

Fig.10–USimageofanovarycyst.

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Co-occurrenceMatrix(GLCM)squareshowsthelargestsub- squares,indicatingthatthemethodissensitivefordetecting malignantthyroidnodules.

Thefollowingclassiﬁerswereusedfordiagnosissupport:

ArtiﬁcialNeuralNetwork(ANN)[34],SupportVectorMachine (SVM)[35],DecisionTree (DT)[36],GaussianMixtureModel (GMM) [37], K-Nearest Neighbour (K-NN) [38], Probabilistic NeuralNetwork(PNN)[39],NaiveBayesClassiﬁer(NBC)[40], SelfOrganizingFeatureMap(SOFM)[41],MultilayerPerceptron Neural Networks (MLPNN) [42], Radial Basis Probabilistic

Neural Network (RBPNN) [43]. Thenext section reportsthe review results of a wide range of texture-based computer supportsystemsusedforcancerdetectionusingUSimages.

4. Review results

Thealgorithmsoutlinedaboveresultfromfundamentaland applied research. In order for these algorithms to become technology, they have to work in more complex systems.

Fig.11–USimagesofabenignthyroidnodule.

Fig.12–USimagesofamalignantthyroid.

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Inthissection,wediscusshowthesealgorithmswereusedin CAD systems. Each of the following sections presents the reviewresultsfortexture-basedCADtargetingaspeciﬁcsoft

tissue cancer. We start the discussion by introducing the reviewresultsforbreastcancerCAD.

4.1. Breastcancer

Table 2 summarizes research work, documented in eight papers, on texture methods for US based breast cancer detection. The table presents four different assessment criteria.Theﬁrstassessmentcriterionisalistofallfeatures used. We have placed a particular emphasis on texture Fig.13–TheofflinesystemisusedtodesigntheonlineordeployedCADsystem.

Fig.14–ErrorplotofnormalizedtexturefeaturesfromUS imagesofbenignandmalignantthyroid.Theblackerror bar indicatesthefeaturemeanandvariancefor featurestakenfromimagesshowingbenignthyroid.

Similarly,theblueerrorbar indicatesthefeature meanandvarianceforfeaturestakenfromimages showingmalignantthyroid.Botherrorbarsarebasedon thenormalizedfeaturevalues.Thelengthofthehorizontal barindicatesthet-value.

Fig.15–ErrorplotofnormalizedtexturefeaturesfromUS imagesofbenignandmalignantthyroid.

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algorithms.ThesealgorithmsaregroupedintermsofGLCM, GrayLevelDifferenceMatrix(GLDM),LBP,FOPandLTE.The nextcolumnstatesthenumberoffeatures.Thisnumberisan important assessment criterion, because it indicates the dimensionality of the feature vector which is fed into the classificationalgorithms.Columnthreestatestheclassifica- tionalgorithmusedforthebreastcancerCADsystem.Thelast columnindicatestheperformanceof thebest classification algorithm.Assuch,the classification performancegivesan indicationofthediagnosticqualitythatcanbeobtainedwitha specific system. However, the performance resultsneed to bequalifiedwithotherassessmentcriteriainordertohave abalancedassessmentoftheresearchwork.

4.2. Prostatecancer

Table3discusessevenworksonprostatecancerusingtexture featuresinUSimages.

4.3. Livercancer

Table4presentstheautomateddiagnosisoffattyliverdisease usingtexturefeaturesinUSimages.Wehavediscussedsix papersusingtexturefeatures.

4.4. Ovariancancer

Table5detailsresearchworkonovariancancerusingtexture features in US images. The four papers discuss the use

oftexturefeaturestoextractdiagnosticallyrelevantinforma- tionfromUSimagesoftheovaries.

4.5. Thyroidcancer

Table6introducesworkonUSbasedthyroidcancerdetection.

Thesixarticlesfocusontexture-basedfeaturestodiscriminate betweennormalandmalignantthyroidnodules.

5. Discussion

US imagesarenon-uniform,they differinterms of feature orientation, feature scale, image resolution and grey level scaling [79,80]. The orientation ambiguity cannot be fixed, becausethetransducerheadisflexible,thepatientandindeed thescannedtissueitselfmove.Theproblemoffeaturescaleis tackled by specifying a ROI. However, the most difficult problem for texturefeatures,from US images,isgrey level scalingandimageresolution,becauserunlengthandGLCM methodsaresensitivetotheseparameters.Inotherwords,the featurevalueschangeinaccordancewithbothspatialandgrey levelscale.Theexactrelationshipbetweenscalechangesand feature value changes is unexplored. There are scaling invariant methods in existence, but the resulting features arenotdiscriminativeenoughforhighqualityCADsystems and,inmanycases,theassociatedalgorithmsarecomputa- tionallycomplex[81].Forexample,LocalConfigurationPattern (LCP)arerotationinvariantandtheyprovidecomplementary informationtoLBP[82].

Fortexturealgorithms,computationalcomplexityisjust equipment investment and computation time. In general, computational complexity of feature extraction algorithms impactonlatencyandcost[83].Thecostrisesinaccordance witheffortstokeepthelatencydown.However,theperfor- manceofprocessingsystemsdoublesevery18months²,hence keeping the computational complexity down is a weak researchobjective.

Classicalmachinelearningsystems,suchastheonesused in current CAD systems, require careful engineering and expertknowledgeinordertocreatefeatureextractors[84].The texture algorithmsextractfeatureinformation from theUS images. Subsequently, the extracted information is fed to machine learningalgorithms.Whilethatprocessisstraight forward in the online system, domain specific expertise is required to test and select the best performing algorithm combination with the design centric offline system. The process of selectingthealgorithm combinationisbasedon experienceaswellasontrialanderror.Themethodoftrialand error impliesthat the resultingCAD systemmightbe sub- optimal.Tobespecific,duringthedesignphaseonlyalimited number of feature algorithms can be tested. Hence, there might be other algorithms which outperform the selected methods.Forexample,adesignermightnotbeawareofthe GLRLM feature extraction methods. That knowledge gap wouldhaveabigimpactonthethyroidcancerclassification example,presentedinSection2.5,becauseaGLRLMmethod (LRE)wasthebestofthetestedmethods.

Fig.16–Treemapofthet-valueresultspresentedinTable A.7ofAppendixA.Thefivemainsquaresinthediagram representthetextureextractionmethodsGLCM,GLRLM, LTE,FOPandLBP.Thenumber,belowthesquarelabel,is thecumulativet-valueresultofthetextureextraction methods.

2Moorslaw,veriﬁedbyobservationsfrom1975onward.

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Angular Second Moment (ASM) Huang et al. 2006

[46]

Benign and malignant breast tissue classiﬁcation through texture features.

Block difference of inverse probabilities (BDIP), block variation of local

correlation coefﬁcients (BVLC) and autocovariance matrix

28 SVM Accuracy: 95.2%

Chen et al. 2002 [47]

GLCM based tissue classiﬁcation. CON, Covariance (COV), Dissimilarity (DISS)

32 DT Accuracy: 87.07%,

Sensitivity: 95.35%, Speciﬁcity: 79.10%

Chang et al. 2003 [48]

Autocovariance coefﬁcients of speckle textures for tissue classiﬁcation.

Speckle autocovariance matrix 24 SVM Accuracy: 93.2%,

Sensitivity: 95.45%, Speciﬁcity: 91.43%

Garra et al. 2003 [49]

Texture analysis to improve the ability of ultrasound to distinguish benign from malignant nodules.

GLCM: Gray Scale Mean (GSM), Variance (VAR), Skewness (SKE), Run percentage (RP),CON,ASM,H,CORR,LRE, Relative frequency of edge elements, Variants of gradients, average absolute value of gradients.

2 Threshold 100% sensitivity, speciﬁcity

was 91% and accuracy was 93%

Shi et al. 2004[50] Texture-based breast mass classiﬁcation.

151 Spatial Gray Level Dependence Matrices (SGLDM) features

13 Fuzzy SVM Accuracy 74.71%,

Sensitivity 88.89% and Speciﬁcity 64.71%.

Gómez et al. 2012 [51]

Sonographic texture analysis to distinguish malignant from benign breast lesions.

GLCM: Autocorrelation (ACORR),CON, CORR1,CORR2, Cluster Prominence (CP), Cluster Shade (CS), (DISS),E,H,HOM1, HOM2, Maximum Probability (MP), Sum Average (SA), Sum Entropy (SENT), Sum VariancesSVAR, Difference Variance (DVAR),DENT, Information Correlation Measure 1 (ICM1), Information Correlation Measure 2 (ICM2), Inverse difference normalized, Inverse difference moment normalized.

22 Fisher Linear Discriminant

Analysis (FLDA)

87% accuracy

sandbiomedicalengineering38(2018)275–296

285

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Table 3–Summary of prostate cancer CAD using texture features in US images.

Author year Objective Features Number of features Classiﬁers Performance

Scheipers et al. 2001[52] GLCM parameter for prostate tissue

characterization. GLCM Details not speciﬁed Spectrum

Parameters of an attenuation model

16 Fuzzy interference system Accuracy 75%

Richard and Keen 1996[53] Texture-based US prostate image segmentation.

LTE

Not discussed Clustering Not applicable for clustering

Mohamed et al. 2003[54] Texture-based malignant

mass detection. Gaborﬁlter texture segmentation.

Magnitude response and spatial smoothing

None –

Basset et al. 1993[55] Detect tissue formations

based on speckle texture. GLCM:ASM,CON,CORR,VAR, inverse difference moment, SA,SVAR,SENT,H,DENT, Information measures of correlation, maximum probability

12 Threshold Sensitivity: 83%, Speciﬁcity: 85%

Han et al. 2008[56] Texture-based cancer

pixel classiﬁcation. multiresolution autocorrelation.

Location, Shape SVM Accuracy 96.4%

Huynen et al. 1994[57] Malignancy detection based on spatial characteristics of image texture.

GLCM: Uniformity,CON, Inverse difference moment, H,CORR

5 DT Sensitivity 80,6%, Speciﬁcity 77.1%

Scheipers et al. 2003[58] Texture-based cancer probability estimation.

GLCM:ASM,CON, CORR, di- mension, inverse difference moment, kappa, peak densi- ty,VAR, Signal to Noise Ratio (SNR)

Spectrum

28 Fuzzy inference system Accuracy 75%

biocyberneticsandbiomedicalengineering38(2018)275–296

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Ravindran, 2008[60]

in focal lesions and normal tissue within the Region of Interest (ROI).

GLCM:SENT,H

GLRLM:LRE, Short Run Emphasis (SRE) Texture Enerrgy Measure (TEM): Spot (S),

Level (L), Edge (ED) Gabor Wavelet

ANN

Correct classiﬁcation: Normal liver lesion: 75%, cystic lesion: 94%, benign lesion: 81%, malignant lesion: 90%

Xian, 2010[61] Differentiation of malignant and benign liver tumours based on the idea that different tissues have different textures.

GLCM:SENT,CON,CORR,H,HOM

5

Fuzzy SVM

Dataset 1(DS1): accuracy: 97%, sensitivity:

100%, Speciﬁcity: 95.45%, Positive Predictive Value (PPV): 91.89% Dataset 2 (DS2): accuracy: 95.11%, sensitivity: 92%, Speciﬁcity: 95.5%, PPV: 85.19%

Acharya et al.

2012[62]

Fatty liver detection based on the idea that the disease changes the

liver tissue texture. GLCM:HOM, Texture Run Length percentage (TexRL)

RUNL:SRE, Gray Level Non-Uniformity (GLNU)

Higher Order Spectra (HOS) Discrete Wavelet Transform (DWT)

3 (SRE, ePRes (12), DWTMean1sym4)

DT

Fuzzy classiﬁer

All features, except the HOS feature, ePRes: Fuzzy (accuracy: 77.3%, PPV:

88.8%, sensitivity: 71.1%, speciﬁcity:

86.7%) All features except the DWT feature DWTMean1sym4: DT (accuracy:

93.3%, PPV: 100%, sensitivity: 88.9%, speciﬁcity: 100%) All features except the texture featureSRE: DT (accuracy: 93.3%, PPV: 100%, sensitivity: 88.9%, speciﬁcity:

100%) All features: DT (accuracy: 93.3%, PPV: 100%, sensitivity: 88.9%, speciﬁcity:

100%)

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Table 4 (Continued)

Author year Objective Features Number of

features

Classiﬁers Performance

Singh et al. 2013 [63]

Fatty liver detection, based on the idea that an increase in the hepatocytes fat content results in a variation of the texture of liver surface.

GLCM ASM, CON, CORR,VAR,IDM, H, SVAR,DVAR,DENT,H(two measures) Gray Level Di_erence Statistics (GLDS),

HOM,CON,E,H FOPGSM SKEKurtosis TEML,ED,S

Statistical Feature Matrix (SFM) Period- icity Roughness Coarseness Contrast Frauenhofer Pattern Sampling (FPS) Ra-

dial sum Angular sum

Fuzzy Hurst exponent (two measures) 7

GLCM

Fuzzy neural network SVM (Radial Basis Func-

tion (RBF) kernel) Back propagation ANN Fuzzy K-NN

Proposed fusing selected features using a linear classiﬁer

The proposed method has an overall classiﬁcation accuracy of 95%

Krishnan et al.

2013[64]

Extracting diagnostically relevant

texture information. GLRLM:SRE,LRE,GLNU, Run Length Non-Uniformity (RLNU),RP, Low Gray- level Run Emphasis (LGRE), High Graylevel Emphasis (HGRE), Short Run Low Gray-level Emphasis (SRLGE), Short Run High Gray-level Emphasis (SRHGE), Long Run Low Gray-level Emphasis (LRLGE), Long Run High Gray-level Emphasis (LRHGE)

11

SVM

Disease Accuracy: Cirrhosis: 84.17%, Fatty: 92.50%, Hcc: 87.88%, Cyst: 100%, Hepatitis: 100% Overall Accuracy: 92.91%

Acharya et al.

2015[7]

Fatty liver classiﬁcation based on the fact that steatosis appears as a diffuse increase in echogenicity resulting from an increase in the parenchymal reﬂectivity. That increase results from the intracellular accumulation of fat- containing vacuoles.

GLRLM,LBP

Not reported

ANN, SVM, K-NN, DT, NBC

Not reported

Acharya et al.

2016[65]

Detecting fatty change based on

US texture. GLRLM, GLRLS, LTE

7

DT, SVM, PNN, K-NN, linear discriminant analysis, quadrature discriminant analysis, NBC

Accuracy: 97.33%, speciﬁcity: 100.00%

and sensitivity: 96.00%

Acharya et al.

2016[66]

Texture methods to characterize and classify the normal and abnormal liver tissues.

GIST descriptors

Clinically

signiﬁcant features DT, SVM, AdaBoost, K-NN, PNN, NBC

With PNN: Accuracy: 98%, speciﬁcity:

100.00% and sensitivity: 96.00%

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LBP LTE

Polynomial order 1,b,c, and RBF kernel

Sensitivity: 100%, Speciﬁcity: 99.8%, PPV:

99.8%

Acharya et al. 2014[69] Texture-based tumor detection.

FOP: Mean (MEA), KUR, VAR

GLCM: CON, ACORR,MP, DISS, HOM, E, CORR, CS, VAR, SA, SENT, SVAR, DVAR,DENT,H(Two measures)

GLRLM: SRE, LRE, GLNU, RLNU, RP, LGRE, HGRE, SRLGE, SRHGE, LRLGE, LRHGE

11

PNN

SVM

DT

K-NN

Detect ovarian tumor with 100% classiﬁcation accuracy, sensitivity, speciﬁcity, and positive predictive value

Khazendar et al. 2015[70] Texture-based detection

of ovarian masses. LBP

187

SVM

K-NN

Performance signiﬁcantly improved to an average accuracy of 0.77 (95% CI:

0.75–0.79) when images were pre-processed, enhanced and treated with a Local Binary Pattern operator (mean difference 0.15: 95% 0.11–0.19, p<0.0001, two-tailedt test).

Acharya et al. 2015[71] Using texture to detect the subtle tissue changes which indicate a malignant tumor.

LBP, LTE

DT, Fuzzy Sugeno, K-NN,

PNN, SVM Not reported

Not reported

ticsandbiomedicalengineering38(2018)275–296

289

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Table 6–Summary of thyroid cancer CAD using texture features in US images.

Author year Objective Features Number of features Classiﬁers Performance

Chang et al. 2010[72] Texture-based thyroid nodule

classiﬁcation. GLCM:CORR,DENT,DVAR, SA,SENT, Sum of Squares (SoS), SVAR, CON, E, H, HOM,CS,CP

Statistical feature matrix GLRLM: SRE, LRE, GLNU,

RLNU,RP LTE

Neighboring grey level dependence matrix,

DWT,

Fourier feature based on local Fourier coefﬁcients.

78 (13 GLCM, 1 Statistical feature matrix, 5 GLRLM, 10 LTE, 5 Neighboring grey level dependence matrix, 12 DWT, 32 Fourier feature based on local Fourier coefﬁcients.

SVM

MLPNN PCA network RBF network SOFM network

SVM has the highest accuracy of 100%.

Acharya et al. 2012 [73]

Texture features to determine thyroid nodule

malignancy.

GLCM:CON,H,HOM

DWT

5 (homogeneity, entropy,

contrast, D2, D1) AdaBoost with

C4_5 configuration Perceptron configuration Pocket configuration Stump

The AdaBoost with perceptron configuration performed the best with classification accuracy, sensitivity, specificity of 100%

Based on the idea that texture indicates the histopathologic components of the thyroid nodules.

GLCM: Symmetry (SYM), H,HOM

DWT

10 (homogeneity, entropy, symmetry, A2, H2, H1, V2,

V1, D2, D1) K-NN

PNN

DT

K-NN: (Accuracy: 98.9%, Sensitivity: 98%, Speciﬁcity: 99.8%)

Determining the risk of malignancy by detecting suspicious ultrasound features with texture methods.

FD

LBP

Fourier Spectrum descrip- tor (FS)

LTE

16 (1 FD, 6LBP, 1 FS, 8 LTE)

SVM:

Linear

Polynomial Order 1 Polynomial Order 2 RBF

Other classiﬁers:

DT

Fuzzy

GMM

K-NN

NBC

PNN

HRUS dataset: SVM and Fuzzy classifiers performed the best with classification accuracy, sensitivity, specificity and positive predictive value of 100%

CEUS dataset: GMM classiﬁer (Accuracy: 98.1%, Sensitivity: 97.2%, Speciﬁcity: 98.9%, Positive predictive value: 98.9%)

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Using

homogeneous and heterogeneous texture to characterize thyroid tissue.

GLCM, LTE,LBP, Fourier spectrum descriptors

GMM, SVM, K-NN, PNN, DT, Adaboost, Fuzzy Sugeno

Not reported

Texture-based feature extraction to classify benign and malignant thyroid nodules.

Gaborﬁlter

30

SVM, K-NN, Multilayer perceptron, DT

Accuracy: 94.3% with DT

ngineering38(2018)275–296

291

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Deep learning eliminates the requirement for domain specific expertise infeature extraction algorithms, because thesemethodsarefedwithrawdata.IncaseofUSbasedsoft tissuecancerdetection,thedeeplearningalgorithmswouldbe fed with unprocessed US images. On the positive side, it eliminates the feature extraction step and the associated ambiguities³[85].Onthenegativeside,thediagnosisprocess becomesveryabstract.Throughoutthedesignoftheoffline system,therearequalitycontrolmeasurestoensuretrace- ability.Thatmeans,ifsomethinggoeswrong,itispossibleto trace backinto thedesign process and locate the error. In contrast, deep learning is a one step process without continuous qualitymonitoring. Ultimately, the decisionon whetherornottousedeeplearningalgorithmswilldependon theirperformanceforwell-knownstandardproblems[86].To bespecific,ifdeeplearningbasedCADsystemsdeliverhigher diagnosis accuracy, when compared to traditional feature based methods,then the newmethods willsupersede the traditionalalgorithmstructures.

5.1. Futurework

ThefuturedirectionofUSimagingcanbesummedupinone word: radiomics [87]. The term refers to the automated extractionand analysisof highdimensionalfeaturevectors [88].Thesefeaturevectors,combinedwithotherpatientdata form the input to sophisticated bioinformaticstools which helptoimprovediagnosticand predictiveaccuracy[89].All processing is done in the digital domain with computer algorithmsexecutedbymicrochips.Theinherentcosteffec- tiveness,speedandscalabilityofthistechnologyhelptocope withaneverincreasingamountofmedicalimages[90].The mainideabehindradiomicsistointerpretmedicalimagesas dataandtheinterpretationofthatdataislefttomachines[91].

Theassumptionisthat,newimageprocessingalgorithmsare abletoextractthesalientfeaturesfromthemedicalimages which are the hidden signatures of the diseases. The postulatedbeneﬁtsfocusonthestrongpointsofcomputing technology. However, these methods are inﬂexible and thereforetheyfailtoaccommodateerrorsintheinputdata.

Inotherwords,radiomicsproposestoreplaceoratleastto

diminishtheroleofthehumanbrain,whichhasanegative impactonbothsafetyandreliabilityofdiseasediagnosis.This poses considerable design challenges, which goes beyond 'good'engineeringpractice[92,93].Radiomicssystemsmustbe safe, reliable and functional [92]. The design process must ensurethatsystemicerrorsareminimized[94].Errorsmaybe causedbyoperatormistakes.Thewidespreaddeploymentof CADsystemswilldependontrustwhichiscloselylinkedto justiﬁcationsgivenforclaimsofreliabilityandsystemsafety [95].

6. Conclusion

Inthispaper,wereviewedUStexturefeaturesforsofttissue cancer detection. The study focused on thyroid, breast, ovarian, liver and prostate cancers. The texture feature extraction algorithmswereintroducedinanexamplestudy on thyroid cancer. In our example, the LRE feature out- performedrestofthealgorithms.However,theresultisnot representative,asitdependsonthetypeofcancerdetection, and on the type of imaging system used. These texture featuresarewidelyusedinthedevelopmentofCADsystems.

Therefore,theCADsystemmustaddresstheneedforaccurate andcosteffectivesofttissuecancerdiagnosis.

Duringthereview,wefoundthattexture-basedfeaturesfor USimageclassiﬁcationisanimportanttopic,becausethese techniquesextractusefulinformation.However,thesetech- niques needtobeusedinconjunctionwithotherfeatures, such asHOS, DWT and statisticalfeatures. Infuture, deep algorithms can beusedto extractthe abstractinformation containedinUSimages,whichmayimprovetheperformance ofUSbasedcancerdetection.

Acknowledgement

AuthorsthankDrMuthuRamaKrishnanMookiahforhelping withthetexturecodes.

3 Featuretestingandselection.

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SA 7.77Eþ00 8.78E01 7.08Eþ00 7.74E01 0.0024 3.1786

SKE 3.98E01 2.66E01 5.79E01 2.96E01 0.0178 2.4424

SRE 1.22E01 2.99E02 1.39E01 2.49E02 0.0209 2.3790

LRLGE 8.95Eþ00 1.57Eþ00 8.04Eþ00 1.60Eþ00 0.0339 2.1747

CS 2.16Eþ01 1.61Eþ01 2.95Eþ01 1.17Eþ01 0.0356 2.1581

KUR 2.38Eþ00 3.17E01 2.62Eþ00 5.18E01 0.0394 2.1197

MP 1.57E01 7.91E02 1.96E01 6.29E02 0.0454 2.0494

HGRE 4.06Eþ01 4.01Eþ01 6.32Eþ01 5.23Eþ01 0.0709 1.8436

LTE5 1.43Eþ07 2.81Eþ06 1.30Eþ07 2.77Eþ06 0.0737 1.8226

SENT 2.52Eþ00 1.46E01 2.46Eþ00 1.30E01 0.0767 1.8039

D 2.38Eþ00 3.30E02 2.36Eþ00 3.61E02 0.0791 1.7887

H 2.90Eþ00 2.17E01 2.80Eþ00 2.10E01 0.0888 1.7319

SRLGE 4.81E02 1.05E02 5.25E02 9.09E03 0.0968 1.6895

SRHGE 1.50Eþ01 3.59Eþ01 2.95Eþ01 3.76Eþ01 0.1374 1.5072

RP 2.47Eþ01 4.16Eþ00 2.31Eþ01 4.53Eþ00 0.1433 1.4844

LTE1 4.82Eþ06 2.72Eþ06 3.82Eþ06 2.45Eþ06 0.1463 1.4733

HOM 8.14E01 3.40E02 8.27E01 3.42E02 0.1549 1.4418

DISS 4.04E01 8.14E02 3.74E01 8.05E02 0.1630 1.4135

LBPEntropy162 3.46Eþ00 9.41E02 3.42Eþ00 1.19E01 0.1685 1.3959

ICM1 -5.14E01 5.13E02 -5.33E01 5.29E02 0.1882 1.3323

DVAR 5.26E01 1.37E01 4.79E01 1.32E01 0.1909 1.3239

CON 5.26E01 1.37E01 4.79E01 1.32E01 0.1909 1.3239

DENT 7.62E01 9.13E02 7.30E01 9.44E02 0.1926 1.3189

LTE6 2.69Eþ05 1.53Eþ05 2.21Eþ05 1.34Eþ05 0.2157 1.2525

E 8.22E02 4.14E02 9.36E02 2.84E02 0.2273 1.2225

LTE2 7.74Eþ05 5.60Eþ05 6.10Eþ05 5.36Eþ05 0.2590 1.1405

GLNU 2.49Eþ04 7.87Eþ03 2.25Eþ04 8.09Eþ03 0.2590 1.1403

RLNU 1.70Eþ04 2.74Eþ03 1.61Eþ04 3.27Eþ03 0.2793 1.0929

LGRE 4.39E01 6.34E02 4.22E01 6.90E02 0.3429 0.9566

LTE20 2.24Eþ06 8.34Eþ05 2.52Eþ06 1.32Eþ06 0.3476 0.9486

LBPEnergy81 1.57E01 2.14E02 1.52E01 2.23E02 0.3629 0.9174

LTE7 4.40Eþ04 3.11Eþ04 3.68Eþ04 2.93Eþ04 0.3660 0.9114

LTE3 4.80Eþ05 3.97Eþ05 3.88Eþ05 3.87Eþ05 0.3747 0.8949

LBPEnergy162 1.56E01 1.90E02 1.61E01 2.92E02 0.4645 0.7374

LTE11 7.22Eþ04 3.94Eþ04 6.46Eþ04 4.03Eþ04 0.4686 0.7297

LTE4 1.30Eþ06 1.19Eþ06 1.09Eþ06 1.14Eþ06 0.5026 0.6747

LTE8 2.07Eþ04 1.53Eþ04 1.82Eþ04 1.58Eþ04 0.5399 0.6168

ICM2 9.25E01 1.33E02 9.27E01 1.27E02 0.5583 0.5889

LBPEnergy243 2.14E01 3.43E02 2.21E01 5.17E02 0.5606 0.5860

LTE12 1.33Eþ04 8.83Eþ03 1.20Eþ04 9.30Eþ03 0.6002 0.5271

LTE21 8.22Eþ04 4.14Eþ04 8.97Eþ04 6.59Eþ04 0.6097 0.5139

LTE10 2.48Eþ06 6.32Eþ05 2.39Eþ06 6.97Eþ05 0.6169 0.5031

LRHGE 5.64Eþ02 3.36Eþ02 6.00Eþ02 3.14Eþ02 0.6787 0.4165

LTE13 6.97Eþ03 5.15Eþ03 6.43Eþ03 5.50Eþ03 0.6994 0.3881

LTE22 2.14Eþ04 1.29Eþ04 2.30Eþ04 1.75Eþ04 0.7037 0.3824

LTE15 1.27Eþ06 3.96Eþ05 1.31Eþ06 5.76Eþ05 0.7466 0.3249

LTE9 4.26Eþ04 4.04Eþ04 3.89Eþ04 4.72Eþ04 0.7498 0.3205

LTE14 1.62Eþ04 1.65Eþ04 1.51Eþ04 1.78Eþ04 0.8164 0.2333

LTE24 7.47Eþ04 7.51Eþ04 7.95Eþ04 8.17Eþ04 0.8182 0.2309

LTE19 1.74Eþ04 1.89Eþ04 1.66Eþ04 1.81Eþ04 0.8678 0.1672

LTE23 1.82Eþ04 1.41Eþ04 1.88Eþ04 1.55Eþ04 0.8824 0.1486

LTE18 5.89Eþ03 4.47Eþ03 5.73Eþ03 4.78Eþ03 0.8998 0.1265

VAR 5.83E02 9.38E03 5.82E02 1.16E02 0.9685 0.0396

LTE17 9.27Eþ03 5.76Eþ03 9.32Eþ03 7.24Eþ03 0.9726 0.0345

LTE16 4.41Eþ04 2.29Eþ04 4.42Eþ04 3.07Eþ04 0.9861 0.0175

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