{"title":"采用AM-FM模型的MRI脑图像分割","authors":"M.S. Patichis, H. Petropoulos, W. Brooks","doi":"10.1109/ACSSC.2000.910646","DOIUrl":null,"url":null,"abstract":"MRI brain images are characterized by non-stationary components that make fully automated segmentation a challenging task. An AM-FM model is used to model these non-stationarities. Using the AM-FM model, a new, fully automated texture segmentation system is used to automatically segment the cerebellum from a 3-D set of MRI brain images.","PeriodicalId":10581,"journal":{"name":"Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154)","volume":"6 1","pages":"906-910 vol.2"},"PeriodicalIF":0.0000,"publicationDate":"2000-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MRI brain image segmentation using an AM-FM model\",\"authors\":\"M.S. Patichis, H. Petropoulos, W. Brooks\",\"doi\":\"10.1109/ACSSC.2000.910646\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"MRI brain images are characterized by non-stationary components that make fully automated segmentation a challenging task. An AM-FM model is used to model these non-stationarities. Using the AM-FM model, a new, fully automated texture segmentation system is used to automatically segment the cerebellum from a 3-D set of MRI brain images.\",\"PeriodicalId\":10581,\"journal\":{\"name\":\"Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154)\",\"volume\":\"6 1\",\"pages\":\"906-910 vol.2\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACSSC.2000.910646\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSSC.2000.910646","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MRI brain images are characterized by non-stationary components that make fully automated segmentation a challenging task. An AM-FM model is used to model these non-stationarities. Using the AM-FM model, a new, fully automated texture segmentation system is used to automatically segment the cerebellum from a 3-D set of MRI brain images.
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