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基于改进的引导滤波和双通道脉冲耦合神经网络的医学图像融合 Title:MedicalImageFusionbasedonImprovedGuidedFilteringandDual-ChannelPulse-CoupledNeuralNetwork Abstract: Medicalimagefusionplaysanessentialroleinmedicaldiagnosisandtreatmentbyintegratingcomplementaryinformationfromdifferentmedicalimagingmodalities.Thispaperproposesanovelapproachformedicalimagefusionusingthecombinationofimprovedguidedfilteringanddual-channelpulse-coupledneuralnetwork(PCNN).Theimprovedguidedfilteringmethodeffectivelypreservesedgedetailsandreducesnoise,whilethedual-channelPCNNaccuratelyhighlightssalientfeatures.Experimentalresultsdemonstratethattheproposedapproachachievessuperiorfusionperformancecomparedtotraditionalmethods,indicatingitspotentialinvariousmedicalapplications. 1.Introduction Medicalimagefusioncombinesmultipleimagesordatasetsacquiredfromdifferentmedicalimagingmodalitiestoprovidecomprehensiveanddetailedinformationformedicaldiagnosisandtreatment.Itenhancestheaccuracyandreliabilityofmedicalimaginganalysisbyintegratingcomplementaryinformation,suchasanatomicalstructureandfunctionalinformation.Traditionalmedicalimagefusionalgorithms,suchasmulti-resolutiondecompositionandpixel-levelfusion,havelimitationsinpreservingstructuraldetailsandsuppressingnoise.Toaddressthesechallenges,thispaperproposesanovelapproachbasedonimprovedguidedfilteringanddual-channelpulse-coupledneuralnetwork(PCNN)formedicalimagefusion. 2.Methodology 2.1ImprovedGuidedFiltering Guidedfilteringisawidelyusedimagefilteringtechniquethatpreservesedgedetailswhilesmoothingtheimage.However,itmaysufferfromover-smoothinganddetailloss.Toovercometheselimitations,animprovedguidedfilteringmethodisintroduced,whichincorporatesanadaptivelocalconstraintderivedfromthevarianceoftheguidedimage.Thisadaptiveconstraintadjuststhefilteringparametersbasedonlocalimagecharacteristics,ensuringbetterpreservationofedgedetailsandsuppressionofnoise. 2.2Dual-ChannelPulse-CoupledNeuralNetwork(PCNN) PCNNisabiologicallyinspiredneuralnetworkmodelthatmimicsthebehaviorofneuronsinthevisualc