Copyright © 2008 The Institute of Electronics, Information and Communication Engineers
Special Section on Signal Processing for Audio and Visual Systems and Its Implementations -- Papers -- Image Processing |
Image Segmentation Using Fuzzy Clustering with Spatial Constraints Based on Markov Random Field via Bayesian Theory*
1 The authors are with the Xi'an Jiaotong University, 710049, P.R. China. E-mail: lixh{at}mailst.xjtu.edu.cn, 2 The author is with the Xi'an Shiyou University, 710065, P.R. China.
Image segmentation is an essential processing step for many image analysis applications. In this paper, a novel image segmentation algorithm using fuzzy C-means clustering (FCM) with spatial constraints based on Markov random field (MRF) via Bayesian theory is proposed. Due to disregard of spatial constraint information, the FCM algorithm fails to segment images corrupted by noise. In order to improve the robustness of FCM to noise, a powerful model for the membership functions that incorporates local correlation is given by MRF defined through a Gibbs function. Then spatial information is incorporated into the FCM by Bayesian theory. Therefore, the proposed algorithm has both the advantages of the FCM and MRF, and is robust to noise. Experimental results on the synthetic and real-world images are given to demonstrate the robustness and validity of the proposed algorithm.
Key Words: Fuzzy C-means clustering (FCM), image segmentation, Markov random field (MRF), Bayesian theory
Manuscript received July 6, 2007. Manuscript revised October 18, 2007.
* This paper was supported by the National Natural Science Foundation of China under Grant No. 9610012.
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