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一种快速的固体核径迹图像分割方法.docx

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一种快速的固体核径迹图像分割方法 Title:ARapidApproachforSolidParticleTrajectoryImageSegmentation Abstract: Particletrajectoryimagingplaysasignificantroleindiversescientificandindustrialfields,includingfluidmechanics,medicaldiagnosis,andmaterialanalysis.Efficientlysegmentingparticletrajectoriesfromcompleximagedataisachallengingtaskduetothepresenceofnoise,occlusions,andvariationsinparticleshapeandsize.Thispaperproposesarapidapproachforsolidparticletrajectoryimagesegmentation,aimedatachievingaccurateandreliableresultsinatime-efficientmanner.Theproposedmethodleveragestheadvantagesofbothtraditionalimageprocessingtechniquesandmoderndeeplearningalgorithms,offeringarobustsolutionforawiderangeofparticletrajectoryimagingapplications. 1.Introduction Particletrajectoryimagingallowsresearchersandengineerstotrackthemovementofsolidparticlesinvariousenvironments.Accuratesegmentationofparticletrajectoriesiscriticalforgainingvaluableinsightsintofluiddynamics,identifyingabnormalitiesinmedicalimages,andcharacterizingmaterialbehavior.Traditionalmanualsegmentationmethodsaretime-consumingandsubjective,necessitatingthedevelopmentofautomatedandefficienttechniques.Thispaperpresentsafastandreliableapproachforsegmentingparticletrajectories,addressingthelimitationsofexistingmethods. 2.Methodology 2.1Pre-processing Theproposedapproachstartswithpre-processingstepstoenhancethequalityoftheinputimages.Thisincludesnoisereduction,contrastadjustment,andimagenormalizationtechniques.Backgroundsubtractionmethodsareemployedtoeliminateirrelevantinformationandenhancethevisibilityofparticletrajectories. 2.2Segmentation Thesegmentationsteputilizesbothtraditionalimageprocessingtechniquesanddeeplearningalgorithms.Initially,athreshold-basedsegmentationtechniqueisappliedtoseparateparticlesfromthebackground.However,duetovariationsinparticleshapeandsize,thistechniquealonemayresultininaccuracies.Totacklethischallenge,aConvolutionalNeuralNetwork(CNN)istrainedusingannotatedparticletrajectoryimages.TheCNNcaneffectivelylearnthevisualfea