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1、AdaptiveFuzzyConnectedness-BasedMedicalImageSegmentationAmolPednekarIoannisA.KakadiarisUdayKurkureVisualComputingLab,Dept.ofComputerScience,Univ.ofHouston,Houston,TX,USAapedneka@bayou.uh.eduioannisk@uh.eduukurkure@bayou.uh.eduAbstracthardbinaryclassification.Theaffinity
2、iscomputedastheweightedsumoftheintensityandtheintensitygradientinInthispaper,wepresentanenhancementofthefuzzytheneighborhoodofthepixeltocapturetheintensityfea-connectedness-basedimagesegmentationmethodbasedturesandpatternsofintensityvariations.However,thisondynamiccom
3、putationofadaptiveweightsfortheho-frameworkassumesuserselectedvaluesfortheassociatedmogeneityandthedirectionalgradientenergyfunctions.weights.Thus,theresultantfuzzyconnectednessmapisAdaptiveweightsenhancetheperformanceandrobustnesssensitivetothecombinationoffeaturesof
4、thetargetregionoftheconventionalfuzzyconnectedness-basedsegmenta-andtheselectedweight.tionwhiledecreasingthedegreeofuserinteraction.TheInthispaper,weproposeemployingdynamicallycom-accuracyoftheadaptivefuzzyconnectedness-basedseg-putedadaptiveweightsforthehomogeneityan
5、dthegradi-mentationoftypicalfuzzymedicalimagesisassessedwithentenergyfunctions,thusdecreasinguserinteraction.Sincerespecttotheconventionalfuzzyconnectedness.theadjacencyofapixeltotheseedpixelinintensityandgradientspacedependsonthelocationofthepixelrelative1.Introducti
6、ontotargetregioncentroid,adaptiveweightsintroduceshift-variancetothedefinitionoffuzzyconnectedness.Inad-Medicalimagecomputinghasrevolutionizedthefieldofdition,weemploythedirectionofthegradientalongwithmedicinebyprovidingnovelmethodstoextractandvisu-itsmagnitude,whichgiv
7、esimportancetovariationsinin-alizeinformationfrommedicaldata,acquiredusingvarioustensityinaspecificdirection.Theremainderofthepa-acquisitionmodalities.Imagesegmentationisoneoftheperisorganizedasfollows:InSection2,weprovideamostimportantstepsasintheanalysisofthepreproce
8、ssedbriefoverviewoftheconventionalfuzzyconnectedness-patientimagedata,whichcanhelpindiagnosis,treatmentbasedimagesegmentatio
