An automatic fuzzy c-means algorithm for image segmentation

被引:41
|
作者
Li, Yan-ling [1 ]
Shen, Yi [1 ]
机构
[1] Huazhong Univ Sci & Technol, Inst Syst Engn, Dept Control Sci & Engn, Wuhan 430074, Peoples R China
基金
高等学校博士学科点专项科研基金; 中国国家自然科学基金;
关键词
Image segmentation; Fuzzy c-means; Fuzzy clustering; K-means; Spatial information; CLUSTER VALIDITY;
D O I
10.1007/s00500-009-0442-0
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm must be estimated by expertise users to determine the cluster number. So, we propose an automatic fuzzy clustering algorithm (AFCM) for automatically grouping the pixels of an image into different homogeneous regions when the number of clusters is not known beforehand. In order to get better segmentation quality, this paper presents an algorithm based on AFCM algorithm, called automatic modified fuzzy c-means cluster segmentation algorithm (AMFCM). AMFCM algorithm incorporates spatial information into the membership function for clustering. The spatial function is the weighted summation of the membership function in the neighborhood of each pixel under consideration. Experimental results show that AMFCM algorithm not only can spontaneously estimate the appropriate number of clusters but also can get better segmentation quality.
引用
收藏
页码:123 / 128
页数:6
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