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

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}
引用次数: 2

摘要

目前,MRI脑图像处理是一个广阔的研究领域,已经使用了几种方法和途径对这些图像进行分割(阈值分割、区域分割、轮廓分割、聚类分割)。在这项工作中,我们提出了一种新的分割方法,该方法基于模糊c均值聚类并使用可能主义者c均值方法。为了验证我们的方法,我们已经成功地在几个真实图像MRI数据集上进行了测试。因此,为了展示我们方法的性能,我们将我们的结果与不同的分割算法进行了比较:k-means、模糊c-means和可能性c-means。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信