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1、StateoftheArtofLevelSetMethodsinSegmentationandRegistrationofMedicalImagingModalitiesElsaAngelini,YinpengJin,AndrewLaineColumbiaUniversity,NewYork,NY,USAABSTRACT:Segmentationofmedicalimagesisanimportantstepinvariousapplicationssuchasvisualization,quantitativea
2、nalysisandimage-guidedsurgery.Numeroussegmentationmethodshavebeendevelopedinthepasttwodecadesforextractionoforgancontoursonmedicalimages.Low-levelsegmentationmethods,suchaspixel-basedclustering,regiongrowing,andfilter-basededgedetection,requireadditionalpre-pr
3、ocessingandpost-processingaswellasconsiderableamountsofexpertinterventionorinformationoftheobjectsofinterest.Furthermorethesubsequentanalysisofsegmentedobjectsishamperedbytheprimitive,pixelorvoxellevelrepresentationsfromthoseregion-basedsegmentation[1].Deforma
4、blemodels,ontheotherhand,provideanexplicitrepresentationoftheboundaryandtheshapeoftheobject.Theycombineseveraldesirablefeaturessuchasinherentconnectivityandsmoothness,whichcounteractnoiseandboundaryirregularities,aswellastheabilitytoincorporateknowledgeaboutth
5、eobjectofinterest[2,3][4].However,parametricdeformablemodelshavetwomainlimitations.First,insituationswheretheinitialmodelanddesiredobjectboundarydiffergreatlyinsizeandshape,themodelmustbere-parameterizeddynamicallytofaithfullyrecovertheobjectboundary.Thesecond
6、limitationisthatithasdifficultydealingwithtopologicaladaptationsuchassplittingormergingmodelparts,ausefulpropertyforrecoveringeithermultipleobjectsorobjectswithunknowntopology.Thisdifficultyiscausedbythefactthatanewparameterizationmustbeconstructedwhenevertopo
7、logychangeoccurs,whichrequiressophisticatedschemes[5,6].Levelsetdeformablemodels[7,8],alsoreferredtoasgeometricdeformablemodels,provideanelegantsolutiontoaddresstheprimarylimitationsofparametricdeformablemodels.Thesemethodshavedrawnagreatdealofattentionsinceth
8、eirintroductionin1988.Advantagesofthecontourimplicitformulationofthedeformablemodeloverparametricformulationinclude:(1)noparameterizationofthecontour,(2)topologicalflexibility,(3)g
