{"title":"MR骨肉瘤图像的分割","authors":"Jincheng Pan, Minglu Li","doi":"10.1109/ICCIMA.2003.1238155","DOIUrl":null,"url":null,"abstract":"There is a large body of literature about MR image segmentation methods. In this paper we briefly review these methods, particular emphasis is based on the relative merits of single image versus multispectral segmentation, and supervised versus unsupervised segmentation methods. Finally, we discuss that how to segment osteosarcoma into tumor tissue classes based on three different MR weighted image parameters (T1, PD, and T2) using unsupervised fuzzy c-means (FCM) clustering algorithm technique for pattern recognition.","PeriodicalId":385362,"journal":{"name":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Segmentation of MR osteosarcoma images\",\"authors\":\"Jincheng Pan, Minglu Li\",\"doi\":\"10.1109/ICCIMA.2003.1238155\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There is a large body of literature about MR image segmentation methods. In this paper we briefly review these methods, particular emphasis is based on the relative merits of single image versus multispectral segmentation, and supervised versus unsupervised segmentation methods. Finally, we discuss that how to segment osteosarcoma into tumor tissue classes based on three different MR weighted image parameters (T1, PD, and T2) using unsupervised fuzzy c-means (FCM) clustering algorithm technique for pattern recognition.\",\"PeriodicalId\":385362,\"journal\":{\"name\":\"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCIMA.2003.1238155\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIMA.2003.1238155","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
There is a large body of literature about MR image segmentation methods. In this paper we briefly review these methods, particular emphasis is based on the relative merits of single image versus multispectral segmentation, and supervised versus unsupervised segmentation methods. Finally, we discuss that how to segment osteosarcoma into tumor tissue classes based on three different MR weighted image parameters (T1, PD, and T2) using unsupervised fuzzy c-means (FCM) clustering algorithm technique for pattern recognition.