Fuzzy c-means clustering with non local spatial information for noisy image segmentation

作     者：Feng Zhao (1) add_zf1119@*** Licheng Jiao (1) Hanqiang Liu (1) 

作者机构：Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Inst Intelligent Informat Proc Xian 710071 Peoples R China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2011年第5卷第1期

页      面：45-56页

核心收录：

学科分类：081603[工学-地图制图学与地理信息工程] 081802[工学-地球探测与信息技术] 07[理学] 08[工学] 080203[工学-机械设计及理论] 070503[理学-地图学与地理信息系统] 0818[工学-地质资源与地质工程] 0705[理学-地理学] 0816[工学-测绘科学与技术] 0802[工学-机械工程] 

基　　金：国家自然科学基金 国家863计划 国家教育部高等学校学科创新引智计划项目 

主　　题：image segmentation fuzzy clustering algo-rithm non local spatial information magnetic resonance(MR) image 

摘      要：As an effective image segmentation method, the standard fuzzy c-means （FCM） clustering algorithm is very sensitive to noise in images. Several modified FCM algorithms, using local spatial information, can overcome this problem to some degree. However, when the noise level in the image is high, these algorithms still cannot obtain satisfactory segmentation performance. In this paper, we introduce a non local spatial constraint term into the objective function of FCM and propose a fuzzy c- means clustering algorithm with non local spatial information （FCM_NLS）. FCM_NLS can deal more effectively with the image noise and preserve geometrical edges in the image. Performance evaluation experiments on synthetic and real images, especially magnetic resonance （MR） images, show that FCM NLS is more robust than both the standard FCM and the modified FCM algorithms using local spatial information for noisy image segmentation.