基于非采样contourlet变换和图论的医学图像侵害

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1、山东大学硕士学位论文ABSTRACTImagesegmentationistheimportantpartinimageprocessingtechnology．Withthedevelopmentoftheinformationtechnology,Medicalimagesegmentationplaysamoreandmoreimportantroleinmedicalapplications．Medicalimagealgorithmefficiencyhasimportanttheoreticalresearchmeaningandcommercialva

2、lue．Inmedicalimages，thereoftenwillbeunnecessarynoisedistributioninfocalregion；thiswillincreasethedifficultyofthedoctortoaccuratelydiagnosisthedisease．Whenthemedicalimageusingageneralsegmentationmethodtosegmentmedicalimage，usuallyneedtodenoisetheimagefirstly,butthedenoiseprogresswillmak

3、ethesegmentationprocesstimelonger,thisrestricttheapplicationofmedicalimagesegmentationtechnology．Fortheabovephenomenon，thispaperproposesthemedicalimagesegmentationbasedonnon—subsampledContourlettransform(NSCT)andgraphtheory．Firstly,usenon—subsampledContourlettransformtheimage，gettheCon

4、tourletcoefficientofmultiscaleanddirectionalinformation．Non—subsampledContourlettransformisdecomposedfastalgorithmofimagewiththedirectionandwiththeshiftinvariance．Imagesegmentationbasedongraphtheoryisahottopicthisyear,becausethegraphtheoryisamaturetheoryinresearchandapplicationrelative

5、ly,thispapermainlyintroducestwokindsofsegmentationmethodbasedonGraphTheory：normalizedcutsegmentationandisoperimetricsegmentationmethod，becausethecuttingmethodbasedonGraphTheoryisgenerallyalargeamountofcalculation，andaffectedbynoise，SObeforethesegmentation，weusingNon-subsampledContourle

6、ttransformextractthelow-frequencyinformationoftheimage，researchshowsthat，thelowfrequencycoefficientsretainthemostoftheinformationintheimage，theunnecessarynoiseisfiltering，anditreducestheredundantinformationinimage．Sointheflowingsegmentationprocess，wecansavealotoftime，andgetagoodsegment

7、ationresults．Theproposedalgorithmisverifiedinthespecialtyofmedicalimage．The山东大学硕士学位论文experimentalresultsshowthat，theproposedalgorithmobtainedgoodresultsandgreatlyshortentheprocessingtime，whichhasanimportantsignificanceinpracticalapplication．Theexperimentalresultsshowthat，therecogniti