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基于稀疏表示的医学图像融合.docx

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基于稀疏表示的医学图像融合 Title:SparseRepresentation-basedFusionofMedicalImages Abstract: Medicalimagefusionplaysacrucialroleinvariousclinicalapplications,includingdiagnosis,treatmentplanning,andsurgicalinterventions.Theaimofimagefusionistoenhancethecomplementaryinformationfrommultiplemedicalimages,leadingtobetterdecision-making.Thispaperproposesanovelapproachformedicalimagefusionbasedonsparserepresentation.Themethodexploitstheinherentsparsityofmedicalimagestoaccuratelyfusetheinformationfromdifferentmodalities,therebyimprovingtheoverallimagequalityandclinicalutility.Experimentalresultsdemonstratetheeffectivenessandsuperiorityoftheproposedapproachcomparedtotraditionalfusionmethods. 1.Introduction(150words) Medicalimagingisarapidlyevolvingfieldthatprovidesimportantinsightsintothediagnosisandtreatmentofvariousdiseases.Imagingmodalitiessuchascomputedtomography(CT),magneticresonanceimaging(MRI),ultrasound,andpositronemissiontomography(PET)offerdifferentperspectivesofanatomicalandphysiologicalinformation.However,eachmodalityhasitslimitationsandcannotprovideacompletepicture.Therefore,thefusionofmultiplemodalitieshasthepotentialtoprovidemoreaccurateandcomprehensiveinformationformedicalpractitioners.Inthispaper,weproposeanovelapproachformedicalimagefusionbasedonsparserepresentation.Sparserepresentationtheoryhasshownpromisingresultsinvariousimageprocessingtasksbycapturingtheunderlyingsparsestructureofsignals.Byexploitingthesparsityofmedicalimages,ourproposedfusionmethodaimstoimprovethevisibilityanddiagnosticperformanceinclinicalapplications. 2.RelatedWork(250words) Asignificantamountofresearchhasbeenconductedinthefieldofmedicalimagefusion,andvarioustechniqueshavebeenproposed.Traditionalfusionmethodsincludepixel-levelmethodssuchasaveraging,weightedaveraging,andintensitymodulation,andfeature-levelmethodssuchaswavelettransform,discretecosinetransform,andprincipalcomponentanalysis.However,thesemethodsoftensufferfrominformationloss,redundancy,andlossoffinedetails,impactingtheclinicalapplicabi