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外文资料-- Diagnosis Technology Research Of Mammographic Masses in Content-based Image Retrieval.PDF外文资料-- Diagnosis Technology Research Of Mammographic Masses in Content-based Image Retrieval.PDF -- 1 元

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DiagnosisTechnologyResearchOfMammographicMassesinContentbasedImageRetrievalSongLixin,WangQingyanCollegeofElectricalElectronicEngineeringHarbinUniversityofScienceandTechnologyHarbin,Chinalixins99yahoo.com.cnWangLiDept.ofgynecologyHeilongjianghospitalHarbin,Chinawangli1439163.comwangqingyan1984163.comAbstractInordertoassistdoctortodiagnosisofmammographicmasses,amethodisproposed.22featuresareextractedfromeachqueriedregionofinterestROI.AknearestneighborKNNalgorithmisusedtoretrievesimilarimagesfromdatabase,andfurthercalculatethemutualinformationMIbetweenthequeriedimageandtheimageswhichareintheretrievalresults,soastoimprovetheretrievalperformance.Finally,theschemetakesthefirstnineimageswiththehighestMIscoresasthefinalretrievalresults.Withthepurposeofprovidingavailabledecisionmakinginformationofdiagnosticaids,wecompareandanalyzethreecalculatingmethodsofdecisionindex.TheExperimentresultsshowthatthemethodisbetterthanmethodofusingKNNonly,andimprovetheaccuracyofdiagnosiseffectively.KeywordsmammographyimagecontentbasedimageretrievalcomputeraideddiagnosismutualinformationI.INTRODUCTIONScreeningmammographyisconsideredthemostreliableandeffectivemethodintheearlydetectionofbreastcancer.However,mammographyimageisblurryanditscontrastislowduetoimagingprinciple,manysmallerlesionsarenoteasytobeobservedandextractedowingtotheoneswhichhavebeenoverwhelmedbythenormalbreasttissue.Therefore,earlydetectionanddiagnosisofbreastcancerreliedheavilyontheradiologistssubjectiveviews.ComputerAidedDetectiontechnologyprovidesanvaluablesecondreview1forradiologistsintheearlydetectionprocessofbreastcancer.ThetraditionalCADsystemofbreastmassesdetectiongenerallybasedonartificialneuralnetwork.ThemethodofContentBasedImageRetrievalnotonlyeliminatedtheburdenofdesigningandtrainingneuralnetworkclassifiers,butalsomadefulluseofpastdiagnosisofbreastmassesoftheoriginalempiricaldata.Accordinglyitcaneffectivelyhelpphysiciansimprovetheaccuracyoftumordiagnosis.AlthoughCBIRhasbeenappliedinanumberoffields,thebreastimageswhicharecharacterizedbyhigherresolution,biggerimagesimilarityandmoreinformationaredifferentfromothers.Itisdifficulttoretrievesimilarimagewiththesamepathologicalcharacteristicsfromimagelibrary.Itisthereforeessentialtoestablisharetrievalsystemwithmedicalpractice.MammographyofCBIRisstillastudyingdirection.Someresearchinstituteshavebeencarriedoutrelevantresearch.BinZheng2hasproposedaninteractiveretrievalmethodwhichimprovedthevisualsimilaritybetweentheresultimagesandthequeriedimages.GeorgiaD.Tourassi3adoptedmutualinformationasthesimilaritymeasurementamongimages,classifiedtheregionofinterestbythemostsimilarimagesofretrieval.HilaryAlto4studiedthevariouscombinationsofshape,textureandedgesharpnessfeatures,whichusedinclassifyingbenignandmalignanttumormasses.Toimprovetheretrievalaccuracyandachievethediagnosis,thispaperstudiesacombinationofKNNMIretrievalmethodofbreastmassesdetection,andgivesadecisionindexofaideddiagnosisonthebasisofthisstudy.II.CONTENTBASEDMAMMOGRAPHICMASSESIMAGERETRIEVALAftersubmittingaqueriedROIimage,thesystemwillautomaticallyextract22featherswhichmatchthefeathersinthedatabase,obtainseveralfrontimagesaccordingtothesimilarityfromhightolow,andfinallycalculatethedecisionindexandanalyzethepathologicalinformationofmassesaccordingtotheretrievalimagesandtheirpathologicalinformation.TheoverallprocessofthemethodisshowninFig1.Figure1.ThediagramoftheoverallprocessFig.2showstheresultoftheretrieval.ThequeriedROIisintheupperleftandthepathologicalinformationisthefollowing.ThefirstnineROIsareintheleftandthelettersaboveeachROImeanclasses,Mmalignant,Bbenign.A.FeatureExtractionTheconventionalcontentbasedretrievalaimedatimprovingvisualsimilaritybetweentheretrievalimagesandthequeriedimage,butthebreastXrayimagesarevisuallyverysimilar,sofeatureselectionprocessshouldnotbebasedsolelyonthevisualsenseofthesimilarities.Doctorsalsotend9781424447138/10/25.00©2010IEEEtolikethesamekindofimagesasthesimilarimages,whichisthesimilarityinthemedicalsense.Therefore,thisfeatureselectionbasedonthefollowingprinciplesifafeatureisvalidinclassification,itisalsovalidinretrieval.Thus,themorewhicharethesamewiththequeriedimagesintheresults,themoreeffectivetheretrievalis.Figure2.TheresultoftheretrievalAfterthequeryROItobeobtained,iftocalculatetherelevantcharacteristicsofasuspiciouslump,thenitrequiressegmentationofsuspiciousmasses.Thesegmentationofsuspiciousmassesisdividedintothreestepsinthistest1.Removethebackgroundtrend5ofthequeriedROIimage2.Restraintheadjacenttissuesofsuspiciousmasses3.SegmentthesuspiciousmasswiththeimprovedMultilayerTopographicSegmentation6.Afterthat,22featuresareextractedfromeachROIasthefeatherset,includingBinZhengsetal2twelvefeathers,NicholasPetricks7sevenfeathersandRenchaoJins8threefeathers.B.SimilarityMeasurementTheimagesimilaritymeasureisthesimilaritybetweentheimagefeatures.Thesimilaritymeasurementmethodswillhaveadirectimpactontheperformanceofimageretrieval.ThispaperproposesacombinationofKNNwithMIsimilaritymatchingalgorithm.1KnearestneighboralgorithmAmultifeatureknearestneighborKNNbasedalgorithmwasappliedtosearchforthecomputationallysimilarROIsinthereferencelibrary.Similaritywasmeasuredbythedifferenceinfeaturevalues,rifxbetweenaqueriedqROIyandareferenceiROIsxinamultidimensionalnfeaturespace,21,nqirqrirdyxfyfx−∑1Thesmallerthedifferencedistance,thehigherthedegreeofthecomputedsimilarityisbetweenanytwocomparedregions.Thecomputeddistancesbetweenatestqueriedregionandeachofthestoredreferenceregionsweresortedrankorderedfromthesmallesttothelargest.ThefirstKregionsinthelistwerethenselectedastheKmostsimilarorthebestmatchedreferenceregions.Adistanceweightwasdefinedas020201,,1,qiidddyxwddd⎧⎪⎪⎨⎪≤⎪⎩Andtheclassificationscore,ortheprobabilitythataregionisactuallymalignant,wascomputedas111MiiMNijijwPww∑∑∑,KMN2WhereNisthenumberofmalignantmassregionsandMisthenumberofbenignmassregionsthatwereselectedinthesetofKmostsimilarROIs.2MaximummutualinformationmethodTheresultsretrievedbyKNNmethodarefurthermatchedbythemaximummutualinformationmethod,andthefinalresultisbetterthanothersbyusingKNNonly.Thecorrelationbetweentworandomvariablesentropy,isalsoknownasmutualinformation.Mutualinformationbetweentworandomvariablescanserveasastatisticalmeasureofcorrelation.Inthepreviousstudy,imageretrieval,mutualinformationalsohasbeenappliedtocontentbasedmedicalimageretrieval,andhasachievedrelativelygoodresults.GiventwoimagesXandY,theirMIIXYisexpressedas2,,,logxyxyxyxyPxyIxyPxyPxPy∑∑3WherePXYx,yisthejointprobabilitydensityfunctionPDFofthetwoimagesbasedontheircorrespondingpixelvalues.PXxandPYyarethemarginalPDFs.Thebasicideaisthatwhentwoimagesarealike,themoreinformationXprovidesforYandviceversa.Therefore,theMIcanbethoughtasanintensitybasedmeasureofimagessimilarity.IfthequeryimageXandastoredimageYdepictsimilarstructures,thenthepixelvalueinimageXshouldbeagoodpredictorofthepixelvalueatthecorrespondinglocationinimageY.Consequently,theirMIshouldbehigh.AsshowninEq.3,theMIestimationoftwomammogramphicROIsrequirescomputationofthejointandmarginalPDFs.Wefollowedthehistogramapproach9forthetask.SincetheimagesofDigitalDatabaseforScreeningMammographyDDSMconsideredinourstudyare12bitimages,thePDFswereestimatedusingareducednumberof256equalsizedintensitybinstoavoidpotentialoverestimationerrors10.ThisisatypicalpracticeforMIestimationinimageregistration.C.DecisionIndexBesidestheretrievedROIimages,thedecisionindexDIindicatingtherelativeprobabilitythataROIcontainsamasscanbecalculatedautomaticallywithaformulaandoutputtotheuser.AhigherDIvaluemeansahigherprobabilitythattheROIcontainsamass.TheformulaforcalculatingtheDIisbasedonthemethodsproposedbyGeorgiaD.Tourassietal3.1211{,1}{,1}{,1}MQIIIQMNQIIQJJIJSYXKRXDIYSYXKRXSYXKRX−−−∑∑∑4WhereMisthenumberofimagesretrievedfromthedatabasethosecontainmassROIs.NisthenumberofimagesretrievedfromthedatabasethosecontainnormalROIs.KMN.RankXIistheorderingnumberofXIwhentheretrievedROIimagesaresortedindescendingorder.Itcanbeseenthatforeithermethod,thehigherDImeansahigherprobabilitythattheROIcontainsamass.DI2consideredthefactoroforderingnumberofXIandassignedarighttoeachofsimilaritymeasurevaluesanditgivesabetterperformanceinourevaluationexperiments,sowetakeitasourinitialdecisionindex.III.EXPERIMENTALRESULTSANDANALYSISROIintheimagedatabasecomesfromDDSMofUniversityofSouthFlorida.TheROIdatabaseincludes514malignantROIsand321benignROIs.EachROIisillustratedinanimagewithsizeof125125pixels.Thedepthofimageis12bits.EachROIcontainsatmostonemass.NomassintheROIweextractedisonthechestwall.Recallrateandprecisionrateisthestandardinformationretrievalevaluationmethod.ThenumberofimagesreturnedKtakesaveryimportanteffectfortheperformanceofKNNretrievalsystem.TheaverageprecisionratewillbecalculatedtoobtainanoptimalKvalue.FromFig.3itisconcludedthatprecisionrateisnotmuchdifferentforthedifferentKvalues.However,consideringthemutualinformationmatchingfortheresults,therelativelysmallandtheprecisionvalueslightlyhighKisselected,K25.Figure3.TheaverageprecisionofdifferentKvaluesAthresholdvalueisusedasadividingpointbetweenbenignandmalignantmasses,andtheFig.4showsthedistributionofbenignandmalignantmassesofdecisionvaluesinthedatabase.Thereisnocleardemarcationpointbetweenthemassesfromthehistogram.Thus,athresholdvalueshouldbedefinedbetween0and1.TheERRORRATEisdefinedbythenumberofmalignantROIofDI<TandthenumberofbenignROIofDI≥T,accordinglybythenumberofwrongdecisionunderT.ItischangingwithT,andcanbefoundoutbyExhaustiveAttackmethod.Figure4.ThehistogramofdecisionindexThenareceiveroperatingcharacteristicROCcurvecanbeplottedtoevaluatetheperformanceofusingoursystemtoclassifybetweentruepositiveandfalsepositivemassregions.TheareaunderROCcurveAUCvalueisusedastheindexofperformance.Theleaveoneout11samplingschemeandtheROCcurveanalysisareusedfortheassessmentofoursystem.EachtimeaROIimageischosenfromthedatabaseFigure5.TheROCcurveoftwomethodsindifferentDiesasthequeryROIimage,thentherestROIimagesformatestdatabase.Theprocedureisperformedrepeatedly,eachtimeaROIimageinthedatabaseischosenasaqueryimage.Fig.5showedtheROCcurveoftwomethodsindifferentDies.Tab.IshowedtheAZvaluesoftwomethodsindifferentDies.AnalysingandcomparingthisthreemethodsDI1addssomeeffectsofsimilarityonthebasisofsequence.PandDI2arebasedonweightedsumofthereciprocalsquareofthedistanceasthemainbasisforsimilaritymatching.DI2addedtheweightedsumofsequencesimilarityplaysacertainroleinimprovingdetectionaccuracyrate.ItcanbeshowedfromTab.IthatDI2isobviouslybetterthantheothers,themethodofKNNMIisbetterthanmethodofusingKNNonly.TABLEI.THEAZVALUESOFTWOMETHODSINDIFFERENTDIESDI1PDI2KNN0.7084±0.01830.7353±0.01740.7452±0.0171KNNMI0.7198±0.01780.7612±0.01660.7984±0.0154EachmammographicimageinDDSMdatabasecontainspathologydiagnosticinformationoflocationofalesion,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.CONCLUSIONSAmethodforcomputeraideddetectionCADofmammographicmassesisproposedandaprototypeCADsystemispresented.ThesystemcanautomaticallyevaluatethepossibilitythataROIismalignantorbenignbyretrievingsimilarROIimagesfromthedatabaseandcalculatingtheDIvalueforeachROI.ThesystemperformanceisevaluatedusingtheleaveoneoutsamplingschemeandROCcurveanalysismethodbasedontheDIsoutputbytheprototypesystem.CBIRbasedCADisausefulmethodforcomputeraideddetectionofmammograhpicmasses.ACKNOWLEDGMENTSTheworkwassupportedbyNaturalScienceFoundationofHeilongJiangProvinceF200912.REFERENCESS1L.J.WarrenBurhenne,S.A.Wood,C.J.DOrsi,S.A.Feig,D.B.Kopans,etal,Potentialcontributionofcomputeraideddetectiontothesensitivityofscreeningmammography,Radiology,vol.215,no.2,pp.554562,2000.2Z.Bin,A.Lu,L.A.Hardesty,J.H.Sumkin,C.M.Hakim,etal.Amethodtoimprovevisualsimilarityofbreastmassesforaninteractivecomputeraideddiagnosisenvironment,.MedicalPhysics,vol.33,no.1,pp.111–117,2006.3G.D.Tourassi,R.VargasVoracek,C.E.Floyd.Contentbasedimageretrievalasacomputeraidforthedetectionofmammographicmasses,InProceedingsofSPIE,5032,pp.590–597,2003.4H.Alto,R.M.Rangayyan,J.E.L.Desautels.Contentbasedretrievalandanalysisofmammographicmasses,JournalofElectronicImaging,vol.14,no.2,pp.023016–1–17,2005.5M.Aoyama,Q.Li,S.Katsuragawa,F.Li,S.Sone,etal.ComputerizedschemefordeterminationofthelikelihoodmeasureofmalignancyforpulmonarynodulesonlowdoseCTimages,MedicalPhysics,vol.30,no,3,pp.387–394,2003.6B.Zheng,Y.H.Chang,D.Gur.ComputerizedDetectionofMassesinDigitizedMammogramsUsingSingleImageSegmentationandaMultilayerTopographicFeatureAnalysis,AcadRadiol,vol.2,no.11,pp.959–966,1995.7N.Petrick,H.P.Chan,D.Wei,B.Sahinet,M.A.Helvie,etal.Automateddetectionofbreastmassesonmammogramsusingadaptivecontrastenhancementandtextureclassification,MedicalPhysics,vol.23,no.10,pp.1685–1695,1996.8R.C.Jin,B.Meng,E.M.Song,X.Y.Xu,L.Jiang,ComputeraidedDetectionofMammographicMassesBasedonContentbasedImageRetrieval,Proc.ofSPIEVol.6514,65141W,2007.9F.Maes,A.Collignon,D.Vandermeulen,G.Marchal,G.Suetens,Multimodalityimageregistrationbymaximizationofmutualinformation,IEEETrans.Med.Imag.,vol.16,pp.187–198,1997.10A.Treves,A.Panzeri,Theupwardbiasinmeasuresofinformationderivedfromlimiteddatasamples,NeuralComputation,vol.7,pp.399–407,1995.11B.Efron,R.J.Tibshirani,AnIntroductiontotheBootstrapM.NewYorkCRCpress,pp.141–150,1993.
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