N. Harchaoui, S. Bara, M. A. Kerroum, A. Hammouch, M. Ouadou, D. Aboutajdine
{"title":"基于可能性c均值算法的改进模糊聚类方法:在医学图像MRI中的应用","authors":"N. Harchaoui, S. Bara, M. A. Kerroum, A. Hammouch, M. Ouadou, D. Aboutajdine","doi":"10.1109/CIST.2012.6388074","DOIUrl":null,"url":null,"abstract":"Currently, the MRI brain image processing is a vast area of research, several methods and approaches have been used to segment these images (thresholding, region, contour, clustering). In this work, we propose a novel segmentation approach, which is based on fuzzy c-means clustering and using possibilist c-means approach. To validate our approach, we have tested successfully on several datasets of real images MRI. Thus, to show the performance of our method, we compared our results with different segmentation algorithms: k-means, fuzzy c-means, and possibilist c-means.","PeriodicalId":120664,"journal":{"name":"2012 Colloquium in Information Science and Technology","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An improved fuzzy clustering approach using possibilist c-means algorithm: Application to medical image MRI\",\"authors\":\"N. Harchaoui, S. Bara, M. A. Kerroum, A. Hammouch, M. Ouadou, D. Aboutajdine\",\"doi\":\"10.1109/CIST.2012.6388074\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Currently, the MRI brain image processing is a vast area of research, several methods and approaches have been used to segment these images (thresholding, region, contour, clustering). In this work, we propose a novel segmentation approach, which is based on fuzzy c-means clustering and using possibilist c-means approach. To validate our approach, we have tested successfully on several datasets of real images MRI. Thus, to show the performance of our method, we compared our results with different segmentation algorithms: k-means, fuzzy c-means, and possibilist c-means.\",\"PeriodicalId\":120664,\"journal\":{\"name\":\"2012 Colloquium in Information Science and Technology\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Colloquium in Information Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIST.2012.6388074\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Colloquium in Information Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIST.2012.6388074","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An improved fuzzy clustering approach using possibilist c-means algorithm: Application to medical image MRI
Currently, the MRI brain image processing is a vast area of research, several methods and approaches have been used to segment these images (thresholding, region, contour, clustering). In this work, we propose a novel segmentation approach, which is based on fuzzy c-means clustering and using possibilist c-means approach. To validate our approach, we have tested successfully on several datasets of real images MRI. Thus, to show the performance of our method, we compared our results with different segmentation algorithms: k-means, fuzzy c-means, and possibilist c-means.