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基于直觉模糊集和亮度增强的医学图像融合.docx

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基于直觉模糊集和亮度增强的医学图像融合 Abstract: Medicalimagefusionplaysacrucialroleinclinicaldecision-making,image-guidedinterventions,andcomputer-aideddiagnosis.However,thefusionofmedicalimagesoftenfaceschallengesregardingtheextractionofusefulinformationfrommultipleinputsourceswhilepreservingimportantdetails.Inthispaper,weproposeanovelapproachformedicalimagefusionbasedonintuitionisticfuzzysetsandbrightnessenhancement.Ourmethodaimstoimprovethequalityoffusedimagesbyeffectivelycombiningthecomplementaryinformationfrommultipleinputsources. 1.Introduction: Medicalimagefusionisatechniquethatcombinescomplementaryinformationfrommultipleinputsources,suchasmagneticresonanceimaging(MRI),computedtomography(CT),andultrasound.Thegoalistoenhancethequalityandvisibilityoffusedimages,therebyassistingradiologistsinmakingaccuratediagnosesandtreatmentdecisions.However,traditionalfusionmethodsoftensufferfromchallengessuchasinformationlossandartifacts.Toaddresstheseissues,weproposeafusionapproachbasedonintuitionisticfuzzysetsandbrightnessenhancement. 2.IntuitionisticFuzzySets(IFS): Intuitionisticfuzzysets(IFS)extendtraditionalfuzzysetsbyconsideringbothmembershipandnon-membershipdegrees.Thisadditionaldegreeenablesamoreflexiblerepresentationofuncertaintyandambiguityinmedicalimages.ByusingIFS,thefusionprocesscancapturetheuncertaintyinherentinmedicaldataandimprovethequalityandreliabilityofthefusedimage. 3.BrightnessEnhancement: Brightnessenhancementisanessentialstepinmedicalimagefusionasitimprovesthevisibilityofkeyfeaturesandstructures.Inourapproach,weemployahistogramequalizationtechniquetoenhancethebrightnessoftheinputimages.Thispreprocessingstepensuresthatimportantdetailsarepreservedandvisibleinthefusedimage. 4.FusionAlgorithm: Ourfusionalgorithmconsistsofthefollowingsteps: 4.1.Preprocessing:Theinputimagesarepreprocessedusinghistogramequalizationtoenhancethebrightnessandcontrast. 4.2.FeatureExtraction:Intuitionisticfuzzysetsareappliedtoextractthefeaturesfromthepreprocessedimages.TheIFSmembershipandnon-member