Brain MR image segmentation using fuzzy clustering with spatial constraints based on Markov random field theory

被引：0

作者：

Feng, YQ ^{[1
]}

Chen, WF ^{[1
]}

机构：

[1] First Mil Med Univ, Dept Biomed Engn, Lab Med Imaging, Guangzhou 510515, Peoples R China

来源：

MEDICAL IMAGING AND AUGMENTED REALITY, PROCEEDINGS | 2004年 / 3150卷

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Unsupervised Fuzzy C-Means (FCM) clustering technique has been widely used in image segmentation. However, conventional FCM algorithm, being a histogram-based method when used in classification, has an intrinsic limitation: no spatial information is taken into account. This causes the FCM algorithm to work only on well-defined images with low level of noise. In this paper, a novel improvement to fuzzy clustering is described. The prior spatial constraint, which is defined as refusable level in this paper, is introduced into FCM algorithm through Markov random field theory and its equivalent Gibbs random field theory, in which the spatial information is encoded through mutual influences of neighboring sites. The algorithm is applied to the segmentation of synthetic image and brain magnetic resonance (MR) images (simulated and real) and the classification results show the new algorithm to be insensitive to noise.

引用

页码：188 / 195

页数：8

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