期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
An Adaptive Fuzzy C-Means Algorithm for Improving MRI Segmentation
1
作者 Elnomery Allam Zanaty 《Open Journal of Medical Imaging》 2013年第4期125-135,共11页
In this paper, we propose new fuzzy c-means method for improving the magnetic resonance imaging (MRI) segmenta- tion. The proposed method called “possiblistic fuzzy c-means (PFCM)” which hybrids the fuzzy c-means (F... In this paper, we propose new fuzzy c-means method for improving the magnetic resonance imaging (MRI) segmenta- tion. The proposed method called “possiblistic fuzzy c-means (PFCM)” which hybrids the fuzzy c-means (FCM) and possiblistic c-means (PCM) functions. It is realized by modifying the objective function of the conventional PCM algorithm with Gaussian exponent weights to produce memberships and possibilities simultaneously, along with the usual point prototypes or cluster centers for each cluster. The membership values can be interpreted as degrees of possibility of the points belonging to the classes, i.e., the compatibilities of the points with the class prototypes. For that, the proposed algorithm is capable to avoid various problems of existing fuzzy clustering methods that solve the defect of noise sensitivity and overcomes the coincident clusters problem of PCM. The efficiency of the proposed algorithm is demonstrated by extensive segmentation experiments by applying them to the challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI) datasets and by comparison with other state of the art algorithms. The experimental results show that the proposed method produces accurate and stable results. 展开更多
关键词 FUZZY CLUSTERING possiblistic C-MEANS MEDICAL Image SEGMENTATION
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部