{"title":"一种新的模糊脑肿瘤分割方法","authors":"N. Castillo, E. Montseny, P. Sobrevilla","doi":"10.1109/FUZZY.2010.5584178","DOIUrl":null,"url":null,"abstract":"In this paper we present a fully automatic and unsupervised brain tumor segmentation method which considers human knowledge. The expert knowledge and the features derived from the MR images are coupled to define heuristic rules aimed to the design of the fuzzy approach. To assess the unsupervised and fully automatic segmentation, intensity-based objective measures are defined, and a new method for obtaining membership functions to suit the MRI data is introduced. The proposed approach is quantitatively comparable to the most accurate existing methods, even though the segmentation is done in 2D.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"31","resultStr":"{\"title\":\"A new fuzzy approach to brain tumor segmentation\",\"authors\":\"N. Castillo, E. Montseny, P. Sobrevilla\",\"doi\":\"10.1109/FUZZY.2010.5584178\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we present a fully automatic and unsupervised brain tumor segmentation method which considers human knowledge. The expert knowledge and the features derived from the MR images are coupled to define heuristic rules aimed to the design of the fuzzy approach. To assess the unsupervised and fully automatic segmentation, intensity-based objective measures are defined, and a new method for obtaining membership functions to suit the MRI data is introduced. The proposed approach is quantitatively comparable to the most accurate existing methods, even though the segmentation is done in 2D.\",\"PeriodicalId\":377799,\"journal\":{\"name\":\"International Conference on Fuzzy Systems\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"31\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Fuzzy Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZY.2010.5584178\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.2010.5584178","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper we present a fully automatic and unsupervised brain tumor segmentation method which considers human knowledge. The expert knowledge and the features derived from the MR images are coupled to define heuristic rules aimed to the design of the fuzzy approach. To assess the unsupervised and fully automatic segmentation, intensity-based objective measures are defined, and a new method for obtaining membership functions to suit the MRI data is introduced. The proposed approach is quantitatively comparable to the most accurate existing methods, even though the segmentation is done in 2D.