A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints

被引：165

作者：

Wang, Jianzhong ^{[1
,3
]}

Kong, Jun ^{[1
,2
]}

Lu, Yinghua ^{[2
]}

Qi, Miao ^{[1
,2
]}

Zhang, Baoxue ^{[1
,3
]}

机构：

[1] NE Normal Univ, Key Lab Appl Stat MOE, Changchun, Peoples R China

[2] NE Normal Univ, Comp Sch, Changchun, Peoples R China

[3] NE Normal Univ, Sch Math & Stat, Changchun, Peoples R China

来源：

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS | 2008年 / 32卷 / 08期

关键词：

Magnetic resonance image; Image segmentation; Fuzzy c-means; Non-local constraint; Spatial constraint;

D O I：

10.1016/j.compmedimag.2008.08.004

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Image segmentation is often required as a preliminary and indispensable stage in the computer aided medical image process, particularly during the clinical analysis of magnetic resonance (MR) brain images. In this paper, we present a modified fuzzy c-means (FCM) algorithm for MRI brain image segmentation. In order to reduce the noise effect during segmentation, the proposed method incorporates both the local spatial context and the non-local information into the standard FCM cluster algorithm using a novel dissimilarity index in place of the usual distance metric. The efficiency of the proposed algorithm is demonstrated by extensive segmentation experiments using both simulated and real MR images and by comparison with other state of the art algorithms. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：685 / 698

页数：14

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