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外文资料-- Diagnosis Technology Research Of Mammographic Masses in Content-based Image Retrieval.PDF

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外文资料-- Diagnosis Technology Research Of Mammographic Masses in Content-based Image Retrieval.PDF

DiagnosisTechnologyResearchOfMammographicMassesinContent-basedImageRetrievalSongLi-xin,WangQing-yanCollegeofElectricalElectronicEngineeringHarbinUniversityofScienceandTechnologyHarbin,Chinalixins99yahoo.com.cnWangLiDept.ofgynecologyHeilongjianghospitalHarbin,Chinawangli1439163.comwangqingyan1984163.comAbstractInordertoassistdoctortodiagnosisofmammo-graphicmasses,amethodisproposed.22featuresareextractedfromeachqueriedregionofinterestROI.Ak-nearestneighborKNNalgorithmisusedtoretrievesimilarimagesfromdatabase,andfurthercalculatethemutualinformationMIbetweenthequeriedimageandtheimageswhichareintheretrievalresults,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,thispaperstudiesacombinationofKNNMIretrievalmethodofbreastmassesdetection,andgivesadecisionindexofaideddiagnosisonthebasisofthisstudy.II.CONTENT-BASEDMAMMOGRAPHICMASSESIMAGERETRIEVALAftersubmittingaqueriedROIimage,thesystemwillautomaticallyextract22featherswhichmatchthefeathersinthedatabase,obtainseveralfrontimagesaccordingtothesimilarityfromhightolow,andfinallycalculatethedecisionindexandanalyzethepathologicalinformationofmassesaccordingtotheretrievalimagesandtheirpathologicalinformation.TheoverallprocessofthemethodisshowninFig1.Figure1.ThediagramoftheoverallprocessFig.2showstheresultoftheretrieval.ThequeriedROIisintheupper-leftandthepathologicalinformationisthefollowing.ThefirstnineROIsareintheleftandthelettersaboveeachROImeanclasses,“M”malignant,“B”benign.A.FeatureExtractionTheconventionalcontent-basedretrievalaimedatimprovingvisualsimilaritybetweentheretrievalimagesandthequeriedimage,butthebreastX-rayimagesarevisuallyverysimilar,sofeatureselectionprocessshouldnotbebasedsolelyonthevisualsenseofthesimilarities.Doctorsalsotend978-1-4244-4713-8/10/25.002010IEEEtolikethesamekindofimagesasthesimilarimages,whichisthesimilarityinthemedicalsense.Therefore,thisfeatureselectionbasedonthefollowingprinciplesifafeatureisvalidinclassification,itisalsovalidinretrieval.Thus,themorewhicharethesamewiththequeriedimagesintheresults,themoreeffectivetheretrievalis.Figure2.TheresultoftheretrievalAfterthequeryROItobeobtained,iftocalculatetherelevantcharacteristicsofasuspiciouslump,thenitrequiressegmentationofsuspiciousmasses.Thesegmentationofsuspiciousmassesisdividedintothreestepsinthistest1.Removethe“backgroundtrend”[5]ofthequeriedROIimage;2.Restraintheadjacenttissuesofsuspiciousmasses;3.SegmentthesuspiciousmasswiththeimprovedMultilayerTopographicSegmentation[6].Afterthat,22featuresareextractedfromeachROIasthefeatherset,includingBinZheng’setal[2]twelvefeathers,NicholasPetrick’s[7]sevenfeathersandRenchaoJin’s[8]threefeathers.B.SimilarityMeasurementTheimagesimilaritymeasureisthesimilaritybetweentheimagefeatures.Thesimilaritymeasurementmethodswillhaveadirectimpactontheperformanceofimageretrieval.ThispaperproposesacombinationofKNNwithMIsimilaritymatchingalgorithm.1K-nearestneighboralgorithmAmulti-featurek-nearestneighborKNNbasedalgorithmwasappliedtosearchforthe“computationallysimilar”ROIsinthereferencelibrary.Similaritywasmeasuredbythedifferenceinfeaturevalues,rifxbetweenaqueriedqROIyandareferenceiROIsxinamultidimensionalnfeaturespace,21,nqirqrirdyxfyfx−∑1Thesmallerthedifference“distance”,thehigherthedegreeofthecomputed“similarity”isbetweenanytwocomparedregions.Thecomputeddistancesbetweenatestqueriedregionandeachofthestoredreferenceregionsweresortedrankorderedfromthesmallesttothelargest.ThefirstKregionsinthelistwerethenselectedastheK“mostsimilar”orthebest“matched”referenceregions.Adistanceweightwasdefinedas020201,,1,qiidddyxwddd⎧⎪⎪⎨⎪≤⎪⎩Andtheclassificationscore,ortheprobabilitythataregionisactuallymalignant,wascomputedas111MiiMNijijwPww∑∑∑,KMN2WhereNisthenumberofmalignantmassregionsandMisthenumberofbenignmassregionsthatwereselectedinthesetofK“mostsimilar”ROIs.2MaximummutualinformationmethodTheresultsretrievedbyKNNmethodarefurthermatchedbythemaximummutualinformationmethod,andthefinalresultisbetterthanothersbyusingKNNonly.Thecorrelationbetweentworandomvariablesentropy,isalsoknownasmutualinformation.Mutualinformationbetweentworandomvariablescanserveasastatisticalmeasureofcorrelation.Inthepreviousstudy,imageretrieval,mutualinformationalsohasbeenappliedtocontent-basedmedicalimageretrieval,andhasachievedrelativelygoodresults.GiventwoimagesXandY,theirMIIX;Yisexpressedas2,,,logxyxyxyxyPxyIxyPxyPxPy∑∑3WherePXYx,yisthejointprobabilitydensityfunctionPDFofthetwoimagesbasedontheircorrespondingpixelvalues.PXxandPYyarethemarginalPDFs.Thebasicideaisthatwhentwoimagesarealike,themoreinformationXprovidesforYandviceversa.Therefore,theMIcanbethoughtasanintensity-basedmeasureofimagessimilarity.IfthequeryimageXandastoredimageYdepictsimilarstructures,thenthepixelvalueinimageXshouldbeagoodpredictorofthepixelvalueatthecorrespondinglocationinimageY.Consequently,theirMIshouldbehigh.AsshowninEq.3,theMIestimationoftwomammo-gramphicROIsrequirescomputationofthejointandmarginalPDFs.Wefollowedthehistogramapproach[9]forthetask.SincetheimagesofDigitalDatabaseforScreeningMammographyDDSMconsideredinourstudyare12-bitimages,thePDFswereestimatedusingareducednumberof256equal-sizedintensitybinstoavoidpotentialoverestimationerrors[10].ThisisatypicalpracticeforMIestimationinimageregistration.C.DecisionIndexBesidestheretrievedROIimages,thedecisionindexDIindicatingtherelativeprobabilitythataROIcontainsamasscanbecalculatedautomaticallywithaformulaandoutputtotheuser.AhigherDIvaluemeansahigherprobabilitythattheROIcontainsamass.TheformulaforcalculatingtheDIisbasedonthemethodsproposedbyGeorgiaD.Tourassietal[3].1211{,1}{,1}{,1}MQIIIQMNQIIQJJIJSYXKRXDIYSYXKRXSYXKRX−−−∑∑∑4WhereMisthenumberofimagesretrievedfromthedatabasethosecontainmassROIs.NisthenumberofimagesretrievedfromthedatabasethosecontainnormalROIs.KMN.RankXIistheorderingnumberofXIwhentheretrievedROIimagesaresortedindescendingorder.Itcanbeseenthatforeithermethod,thehigherDImeansahigherprobabilitythattheROIcontainsamass.DI2consi-deredthefactoroforderingnumberofXIandassignedarighttoeachofsimilaritymeasurevaluesanditgivesabetterper-formanceinourevaluationexperiments,sowetakeitasourinitialdecisionindex.III.EXPERIMENTALRESULTSANDANALYSISROIintheimagedatabasecomesfromDDSMofUniversityofSouthFlorida.TheROIdatabaseincludes514malignantROIsand321benignROIs.EachROIisillustratedinanimagewithsizeof125125pixels.Thedepthofimageis12bits.EachROIcontainsatmostonemass.NomassintheROIweextractedisonthechestwall.Recallrateandprecisionrateisthestandardinformationretrievalevaluationmethod.ThenumberofimagesreturnedKtakesaveryimportanteffectfortheperformanceofKNNretrievalsystem.TheaverageprecisionratewillbecalculatedtoobtainanoptimalKvalue.FromFig.3itisconcludedthatprecisionrateisnotmuchdifferentforthedifferentKvalues.However,consideringthemutualinformationmatchingfortheresults,therelativelysmallandtheprecisionvalueslightlyhighKisselected,K25.Figure3.TheaverageprecisionofdifferentKvaluesAthresholdvalueisusedasadividingpointbetweenbenignandmalignantmasses,andtheFig.4showsthedistributionofbenignandmalignantmassesofdecisionvaluesinthedatabase.Thereisnocleardemarcationpointbetweenthemassesfromthehistogram.Thus,athresholdvalueshouldbedefinedbetween0and1.The“ERRORRATE”isdefinedbythenumberofmalignantROIofDI<TandthenumberofbenignROIofDI≥T,accordinglybythenumberofwrongdecisionunderT.ItischangingwithT,andcanbefoundoutbyExhaustiveAttackmethod.Figure4.ThehistogramofdecisionindexThenareceiveroperatingcharacteristicROCcurvecanbeplottedtoevaluatetheperformanceofusingoursystemtoclassifybetweentrue-positiveandfalse-positivemassregions.TheareaunderROCcurveAUCvalueisusedastheindexofperformance.Theleave-one-out[11]samplingschemeandtheROCcurveanalysisareusedfortheassessmentofoursystem.EachtimeaROIimageischosenfromthedatabaseFigure5.TheROCcurveoftwomethodsindifferentDiesasthequeryROIimage,thentherestROIimagesformatestdata-base.Theprocedureisperformedrepeatedly,eachtimeaROIimageinthedatabaseischosenasaqueryimage.Fig.5showedtheROCcurveoftwomethodsindifferentDies.Tab.IshowedtheAZvaluesoftwomethodsindifferentDies.AnalysingandcomparingthisthreemethodsDI1addssomeeffectsofsimilarityonthebasisofsequence.PandDI2arebasedonweightedsumofthereciprocalsquareofthedistanceasthemainbasisforsimilaritymatching.DI2addedtheweightedsumofsequencesimilarityplaysacertainroleinimprovingdetectionaccuracyrate.ItcanbeshowedfromTab.IthatDI2isobviouslybetterthantheothers,themethodofKNNMIisbetterthanmethodofusingKNNonly.TABLEI.THEAZVALUESOFTWOMETHODSINDIFFERENTDIESDI1PDI2KNN0.70840.01830.73530.01740.74520.0171KNNMI0.71980.01780.76120.01660.79840.0154EachmammographicimageinDDSMdatabasecontainspathologydiagnosticinformationoflocationofalesion,whichisgivenbyseveralradiologistsbasedontheirexperiencesofyearsinthediagnosis.Then,intheROIresults,weextractedpathologyinformationforeachROIrespectivelyfromthepathologymessagedata,includingDensity,Shape,Margins,Assessment,Subtlety,etc.Tab.IIshowsthatthecomparativeprecisionineachpathologicalinformationintwomethods.TABLEII.THEPRECISIONOFEVERYPATHOLOGYINFORMATIONINTWOMETHODSDensityShapeMarginsAssessmentSubtletyKNN62.1570.5871.4667.2572.53KNNMI65.4876.9778.6561.3669.57ItisclearfromthesedatathatthereisacertaindifferenceButingeneralnotintwodifferentretrievalmethodsforthedifferentpathologicalinformation.Therefore,themethodofKNNMIcanbringagreatreferencevalueforadoctorinthepathologicaldiagnosisandprovideagreathelptotheearlydiagnosisofbreastmasslesions.IV.CONCLUSIONSAmethodforcomputer-aideddetectionCADofmammographicmassesisproposedandaprototypeCADsystemispresented.ThesystemcanautomaticallyevaluatethepossibilitythataROIismalignantorbenignbyretrievingsimilarROIimagesfromthedatabaseandcalculatingtheDIvalueforeachROI.Thesystemperformanceisevaluatedusingtheleave-one-outsamplingschemeandROCcurveanalysismethodbasedontheDIsoutputbytheprototypesystem.CBIR-basedCADisausefulmethodforcomputer-aideddetectionofmammograhpicmasses.ACKNOWLEDGMENTSTheworkwassupportedbyNaturalScienceFoundationofHeilongJiangProvinceF200912.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,”InProceedingsofSPIE,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].NewYorkCRCpress,pp.141–150,1993.

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