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DiagnosisTechnologyResearchOfMammographicMassesinContent-basedImageRetrievalSongLi-xin,WangQing-yanCollegeofElectrical&ElectronicEngineeringHarbinUniversityofScienceandTechnologyHarbin,[email protected]pitalHarbin,[email protected]@163.comAbstract—Inordertoassistdoctortodiagnosisofmammo-graphicmasses,amethodisproposed.22featuresareextractedfromeachqueriedregionofinterest(ROI).Ak-nearestneighbor(KNN)algorithmisusedtoretrievesimilarimagesfromdatabase,andfurthercalculatethemutualinformation(MI)betweenthequeriedimageandtheimageswhichareintheretrievalresults,soastoimprovetheretrievalperformance.Finally,theschemetakesthefirstnineimageswiththehighestMIscoresasthefinalretrievalresults.Withthepurposeofprovidingavailabledecision-makinginformationofdiagnosticaids,wecompareandanalyzethreecalculatingmethodsofdecisionindex.TheExperimentresultsshowthatthemethodisbetterthanmethodofusingKNNonly,andimprovetheaccuracyofdiagnosiseffectively.Keywords-mammographyimage;content-basedimagere-trieval;computer-aideddiagnosis;mutualinformationI.INTRODUCTIONScreeningmammographyisconsideredthemostreliableandeffectivemethodintheearlydetectionofbreastcancer.However,mammographyimageisblurryanditscontrastislowduetoimagingprinciple,manysmallerlesionsarenoteasytobeobservedandextractedowingtotheoneswhichhavebeenoverwhelmedbythenormalbreasttissue.Therefore,earlydetectionanddiagnosisofbreastcancerreliedheavilyontheradiologist’ssubjectiveviews.Computer-AidedDetectiontechnologyprovidesanvaluable“secondreview”[1]forradiologistsintheearlydetectionprocessofbreastcancer.ThetraditionalCADsystemofbreastmassesdetectiongenerallybasedonartificialneuralnetwork.ThemethodofContent-BasedImageRetrievalnotonlyeliminatedtheburdenofdesigningandtrainingneuralnetworkclassifiers,butalsomadefulluseofpastdiagnosisofbreastmassesoftheoriginalempiricaldata.Accordinglyitcaneffectivelyhelpphysiciansimprovetheaccuracyoftumordiagnosis.AlthoughCBIRhasbeenappliedinanumberoffields,thebreastimageswhicharecharacterizedbyhigherresolution,biggerimagesimilarityandmoreinformationaredifferentfromothers.Itisdifficulttoretrievesimilarimagewiththesamepathologicalcharacteristicsfromimagelibrary.Itisthereforeessentialtoestablisharetrievalsystemwithmedicalpractice.MammographyofCBIRisstillastudyingdirection.Someresearchinstituteshavebeencarriedoutrelevantresearch.BinZheng[2]hasproposedaninteractiveretrievalmethodwhichimprovedthevisualsimilaritybetweentheresultimagesandthequeriedimages.GeorgiaD.Tourassi[3]adoptedmutualinformationasthesimilaritymeasurementamongimages,classifiedtheregionofinterestbythemostsimilarimagesofretrieval.HilaryAlto[4]studiedthevariouscombinationsofshape,textureandedgesharpnessfeatures,whichusedinclassifyingbenignandmalignanttumormasses.Toimprovetheretrievalaccuracyandachievethediagnosis,thispaperstudiesacombinationofKNN+MIretrievalmethodofbreastmassesdetection,andgivesadecisionindexofaideddiagnosisonthebasisofthisstudy.II.CONTENT-BASEDMAMMOGRAPHICMASSESIMAGERETRIEVALAftersubmittingaqueriedROIimage,thesystemwillautomaticallyextract22featherswhichmatchthefeathersinthedatabase,obtainseveralfrontimagesaccordingtothesimilarityfromhightolow,andfinallycalculatethedecisionindexandanalyzethepathologicalinformationofmassesaccordingtotheretrievalimagesandtheirpathologicalinformation.TheoverallprocessofthemethodisshowninFig1.Figure1.ThediagramoftheoverallprocessFig.2showstheresultoftheretrieval.ThequeriedROIisintheupper-leftandthepathologicalinformationisthefollowing.ThefirstnineROIsareintheleftandthelettersaboveeachROImeanclasses,“M”:malignant,“B”:benign.A.FeatureExtractionTheconventionalcontent-basedretrievalaimedatimprovingvisualsimilaritybetweentheretrievalimagesandthequeriedimage,butthebreastX-rayimagesarevisuallyverysimilar,sofeatureselectionprocessshouldnotbebasedsolelyonthevisualsenseofthesimilarities.Doctorsalsotend978-1-4244-4713-8/10/$25.00©2010IEEEtolikethesamekindofimagesasthesimilarimages,whichisthesimilarityinthemedicalsense.Therefore,thisfeatureselectionbasedonthefollowingprinciples:ifafeatureisvalidinclassification,itisalsovalidinretrieval.Thus,themorewhicharethesamewiththequeriedimagesintheresults,themoreeffectivetheretrievalis.Figure2.TheresultoftheretrievalAfterthequeryROItobeobtained,iftocalculatetherelevantcharacteristicsofasuspiciouslump,thenitrequiressegmentationofsuspiciousmasses.Thesegmentationofsuspiciousmassesisdividedintothreestepsinthistest:1.Removethe“backgroundtrend”[5]ofthequeriedROIimage;2.Restraintheadjacenttissuesofsuspiciousmasses;3.Segmentthesuspiciousmasswiththeimproved"MultilayerTopographicSegmentation"[6].Afterthat,22featuresareextractedfromeachROIasthefeatherset,includingBinZheng’setal[2]twelvefeathers,NicholasPetrick’s[7]sevenfeathersandRenchaoJin’s[8]threefeathers.B.SimilarityMeasurementTheimagesimilaritymeasureisthesimilaritybetweentheimagefeatures.Thesimilaritymeasurementmethodswillhaveadirectimpactontheperformanceofimageretrieval.ThispaperproposesacombinationofKNNwithMIsimilaritymatchingalgorithm.1)K-nearestneighboralgorithm:Amulti-featurek-nearestneighbor(KNN)basedalgorithmwasappliedtosearchforthe“computationallysimilar”ROIsinthereferencelibrary.Similaritywasmeasuredbythedifferenceinfeaturevalues,()rifxbetweenaqueried()qROIyandareference()iROIsxinamultidimensional(n)featurespace,21(,)(()()nqirqrirdyxfyfx==−∑(1)Thesmallerthedifference(“distance”),thehigherthedegreeofthecomputed“similarity”isbetweenanytwocomparedregions.Thecomputeddistancesbetweenatest(queried)regionandeachofthestoredreferenceregionsweresorted(rankordered)fromthesmallesttothelargest.ThefirstKregionsinthelistwerethenselectedastheK“mostsimilar”(orthebest“matched”)referenceregions.Adistanceweightwasdefinedas020201,(,)1,qiidddyxwddd⎧>⎪⎪=⎨⎪≤⎪⎩Andtheclassificationscore,ortheprobabilitythataregionisactuallymalignant,wascomputedas111MiiMNijijwPww====+∑∑∑,KMN=+(2)WhereNisthenumberofmalignantmassregionsandMisthenumberofbenignmassregionsthatwereselectedinthesetofK“mostsimilar”ROIs.2)Maximummutualinformationmethod:TheresultsretrievedbyKNNmethodarefurthermatchedbythemaximummutualinformationmethod,andthefinalresultisbetterthanothersbyusingKNNonly.Thecorrelationbetweentworandomvariablesentropy,isalsoknownasmutualinformation.Mutualinformationbetweentworandomvariablescanserveasastatisticalmeasureofcorrelation.Inthepreviousstudy,imageretrieval,mutualinformationalsohasbeenappliedtocontent-basedmedicalimageretrieval,andhasachievedrelativelygoodresults.GiventwoimagesXandY,theirMII(X;Y)isexpressedas:2(,)(,)(,)log()()xyxyxyxyPxyIxyPxyPxPy=∑∑(3)WherePXY(x,y)isthejointprobabilitydensityfunction(PDF)ofthetwoimagesbasedontheircorrespondingpixelvalues.PX(x)andPY(y)arethemarginalPDFs.Thebasicideaisthatwhentwoimagesarealike,themoreinformationXprovidesforYandviceversa.Therefore,theMIcanbethoughtasanintensity-basedmeasureofimagessimilarity.IfthequeryimageXandastoredimageYdepictsimilarstructures,thenthepixelvalueinimageXshouldbeagoodpredictorofthepixelvalueatthecorrespondinglocationinimageY.Consequently,theirMIshouldbehigh.AsshowninEq.3,theMIestimationoftwomammo-gramphicROIsrequirescomputationofthejointandmarginalPDFs.Wefollowedthehistogramapproach[9]forthetask.SincetheimagesofDigitalDatabaseforScreeningMammography(DDSM)consideredinourstudyare12-bitimages,thePDFswereestimatedusingareducednumberof256equal-sizedintensitybinstoavoidpotentialoverestimationerrors[10].ThisisatypicalpracticeforMIestimationinimageregistration.C.DecisionIndexBesidestheretrievedROIimages,thedecisionindex(DI)indicatingtherelativeprobabilitythataROIcontainsamasscanbecalculatedautomaticallywithaformulaandoutputtotheuser.AhigherDIvaluemeansahigherprobabilitythattheROIcontainsamass.TheformulaforcalculatingtheDIisbasedonthemethodsproposedbyGeorgiaD.Tourassietal[3].1211{(,)(1())}(){(,)(1())}{(,)(1())}MQIIIQMNQIIQJJIJSYXKRXDIYSYXKRXSYXKRX===×+−=×+−+×+−∑∑∑(4)WhereMisthenumberofimagesretrievedfromthedatabasethosecontainmassROIs.NisthenumberofimagesretrievedfromthedatabasethosecontainnormalROIs.K=M+N.Rank(XI)istheorderingnumberofXIwhentheretrievedROIimagesaresortedindescendingorder.Itcanbeseenthatforeithermethod,thehigherDImeansahigherprobabilitythattheROIcontainsamass.DI2consi-deredthefactoroforderingnumberofXIandassignedarighttoeachofsimilaritymeasurevaluesanditgivesabetterper-formanceinourevaluationexperiments,sowetakeitasourinitialdecisionindex.III.EXPERIMENTALRESULTSANDANALYSISROIintheimagedatabasecomesfromDDSMofUniversityofSouthFlorida.TheROIdatabaseincludes514malignantROIsand321benignROIs.EachROIisillustratedinanimagewithsizeof125×125pixels.Thedepthofimageis12bits.EachROIcontainsatmostonemass.NomassintheROIweextractedisonthechestwall.Recallrateandprecisionrateisthestandardinformationretrievalevaluationmethod.ThenumberofimagesreturnedKtakesaveryimportanteffectfortheperformanceofKNNretrievalsystem.TheaverageprecisionratewillbecalculatedtoobtainanoptimalKvalue.FromFig.3itisconcludedthatprecisionrateisnotmuchdifferentforthedifferentKvalues.However,consideringthemutualinformationmatchingfortheresults,therelativelysmallandtheprecisionvalueslightlyhighKisselected,K=25.Figure3.TheaverageprecisionofdifferentKvaluesAthresholdvalueisusedasadividingpointbetweenbenignandmalignantmasses,andtheFig.4showsthedistributionofbenignandmalignantmassesofdecisionvaluesinthedatabase.Thereisnocleardemarcationpointbetweenthemassesfromthehistogram.Thus,athresholdvalueshouldbedefinedbetween0and1.The“ERRORRATE”isdefinedbythenumberofmalignantROIofDI<TandthenumberofbenignROIofDI≥T,accordinglybythenumberofwrongdecisionunderT.ItischangingwithT,andcanbefoundoutbyExhaustiveAttackmethod.Figure4.ThehistogramofdecisionindexThenareceiveroperatingcharacteristic(ROC)curvecanbeplottedtoevaluatetheperformanceofusingoursystemtoclassifybetweentrue-positiveandfalse-positivemassregions.TheareaunderROCcurve(AUCvalue)isusedastheindexofperformance.Theleave-one-out[11]samplingschemeandtheROCcurveanalysisareusedfortheassessmentofoursystem.EachtimeaROIimageischosenfromthedatabaseFigure5.TheROCcurveoftwomethodsindifferentDiesasthequeryROIimage,thentherestROIimagesformatestdata-base.Theprocedureisperformedrepeatedly,eachtimeaROIimageinthedatabaseischosenasaqueryimage.Fig.5showedtheROCcurveoftwomethodsindifferentDies.Tab.IshowedtheAZvaluesoftwomethodsindifferentDies.Analysingandcomparingthisthreemethods:DI1addssomeeffectsofsimilarityonthebasisofsequence.PandDI2arebasedonweightedsumofthereciprocalsquareofthedistanceasthemainbasisforsimilaritymatching.DI2addedtheweightedsumofsequencesimilarityplaysacertainroleinimprovingdetectionaccuracyrate.ItcanbeshowedfromTab.IthatDI2isobviouslybetterthantheothers,themethodofKNN+MIisbetterthanmethodofusingKNNonly.TABLEI.THEAZVALUESOFTWOMETHODSINDIFFERENTDIESDI1PDI2KNN0.7084±0.01830.7353±0.01740.7452±0.0171KNN+MI0.7198±0.01780.7612±0.01660.7984±0.0154EachmammographicimageinDDSMdatabasecontainspathologydiagnosticinformationoflocationofalesion,whichisgivenbyseveralradiologistsbasedontheirexperiencesofyearsinthediagnosis.Then,intheROIresults,weextractedpathologyinformationforeachROIrespectivelyfromthepathologymessagedata,includingDensity,Shape,Margins,Assessment,Subtlety,etc.Tab.IIshowsthatthecomparativeprecisionineachpathologicalinformationintwomethods.TABLEII.THEPRECISIONOFEVERYPATHOLOGYINFORMATIONINTWOMETHODSDensityShapeMarginsAssessmentSubtletyKNN62.15%70.58%71.46%67.25%72.53%KNN+MI65.48%76.97%78.65%61.36%69.57%ItisclearfromthesedatathatthereisacertaindifferenceButingeneralnotintwodifferentretrievalmethodsforthedifferentpathologicalinformation.Therefore,themethodofKNN+MIcanbringagreatreferencevalueforadoctorinthepathologicaldiagnosisandprovideagreathelptotheearlydiagnosisofbreastmasslesions.IV.CONCLUSIONSAmethodforcomputer-aideddetection(CAD)ofmammographicmassesisproposedandaprototypeCADsystemispresented.ThesystemcanautomaticallyevaluatethepossibilitythataROIismalignantorbenignbyretrievingsimilarROIimagesfromthedatabaseandcalculatingtheDIvalueforeachROI.Thesystemperformanceisevaluatedusingtheleave-one-outsamplingschemeandROCcurveanalysismethodbasedontheDIsoutputbytheprototypesystem.CBIR-basedCADisausefulmethodforcomputer-aideddetectionofmammograhpicmasses.ACKNOWLEDGMENTSTheworkwassupportedbyNaturalScienceFoundationofHeilongJiangProvince(F200912).REFERENCESS[1]L.J.WarrenBurhenne,S.A.Wood,C.J.D’Orsi,S.A.Feig,D.B.Kopans,etal,“Potentialcontributionofcomputer-aideddetectiontothesensitivityofscreeningmammography,”Radiology,vol.215,no.2,pp.554-562,2000.[2]Z.Bin,A.Lu,L.A.Hardesty,J.H.Sumkin,C.M.Hakim,etal.“Amethodtoimprovevisualsimilarityofbreastmassesforaninteractivecomputer-aideddiagnosisenvironment,”.MedicalPhysics,vol.33,no.1,pp.111–117,2006.[3]G.D.Tourassi,R.Vargas-Voracek,C.E.Floyd.“Content-basedimageretrievalasacomputeraidforthedetectionofmammographicmasses,”In:ProceedingsofSPIE,5032,pp.590–597,2003.[4]H.Alto,R.M.Rangayyan,J.E.L.Desautels.“Content-basedretrievalandanalysisofmammographicmasses,”JournalofElectronicImaging,vol.14,no.2,pp.023016–1–17,2005.[5]M.Aoyama,Q.Li,S.Katsuragawa,F.Li,S.Sone,etal.”Computerizedschemefordeterminationofthelikelihoodmeasureofmalignancyforpulmonarynodulesonlow-doseCTimages,”MedicalPhysics,vol.30,no,3,pp.387–394,2003.[6]B.Zheng,Y.H.Chang,D.Gur.”ComputerizedDetectionofMassesinDigitizedMammogramsUsingSingle-ImageSegmentationandaMultilayerTopographicFeatureAnalysis,”AcadRadiol,vol.2,no.11,pp.959–966,1995.[7]N.Petrick,H.P.Chan,D.Wei,B.Sahinet,M.A.Helvie,etal.“Automateddetectionofbreastmassesonmammogramsusingadaptivecontrastenhancementandtextureclassification,”MedicalPhysics,vol.23,no.10,pp.1685–1695,1996.[8]R.C.Jin,B.Meng,E.M.Song,X.Y.Xu,L.Jiang,“Computer-aidedDetectionofMammographicMassesBasedonContent-basedImageRetrieval”,Proc.ofSPIEVol.6514,65141W,2007.[9]F.Maes,A.Collignon,D.Vandermeulen,G.Marchal,G.Suetens,"Multimodalityimageregistrationbymaximizationofmutualinformation,"IEEETrans.Med.Imag.,vol.16,pp.187–198,1997.[10]A.Treves,A.Panzeri,“Theupwardbiasinmeasuresofinformationderivedfromlimiteddatasamples,”NeuralComputation,vol.7,pp.399–407,1995.[11]B.Efron,R.J.Tibshirani,AnIntroductiontotheBootstrap[M].NewYork:CRCpress,pp.141–150,1993.
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