{"title":"医学图像分割中的模糊聚类技术","authors":"M. Tabakov","doi":"10.1109/ISEFS.2006.251140","DOIUrl":null,"url":null,"abstract":"The main objective of medical image segmentation is to extract and characterise anatomical structures with respect to some input features or expert knowledge. This paper describes a way of medical image segmentation using an appropriately defined fuzzy clustering method based on a fuzzy similarity relation. The considered relation is defined in terms of the Euclidean metric. A fuzzy similarity relation-based image segmentation algorithm is also introduced. To illustrate the obtained segmentation process some examples of computed tomography imaging are considered. Some results, using the classical fuzzy c-means clustering algorithm are also presented, for a comparison purpose","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"37","resultStr":"{\"title\":\"A Fuzzy Clustering Technique for Medical Image Segmentation\",\"authors\":\"M. Tabakov\",\"doi\":\"10.1109/ISEFS.2006.251140\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The main objective of medical image segmentation is to extract and characterise anatomical structures with respect to some input features or expert knowledge. This paper describes a way of medical image segmentation using an appropriately defined fuzzy clustering method based on a fuzzy similarity relation. The considered relation is defined in terms of the Euclidean metric. A fuzzy similarity relation-based image segmentation algorithm is also introduced. To illustrate the obtained segmentation process some examples of computed tomography imaging are considered. Some results, using the classical fuzzy c-means clustering algorithm are also presented, for a comparison purpose\",\"PeriodicalId\":269492,\"journal\":{\"name\":\"2006 International Symposium on Evolving Fuzzy Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"37\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 International Symposium on Evolving Fuzzy Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISEFS.2006.251140\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 International Symposium on Evolving Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISEFS.2006.251140","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Fuzzy Clustering Technique for Medical Image Segmentation
The main objective of medical image segmentation is to extract and characterise anatomical structures with respect to some input features or expert knowledge. This paper describes a way of medical image segmentation using an appropriately defined fuzzy clustering method based on a fuzzy similarity relation. The considered relation is defined in terms of the Euclidean metric. A fuzzy similarity relation-based image segmentation algorithm is also introduced. To illustrate the obtained segmentation process some examples of computed tomography imaging are considered. Some results, using the classical fuzzy c-means clustering algorithm are also presented, for a comparison purpose