Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Spatial models for fuzzy clustering
Computer Vision and Image Understanding
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
An Extensible MRI Simulator for Post-Processing Evaluation
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
A spatially constrained mixture model for image segmentation
IEEE Transactions on Neural Networks
A robust segmentation method for the AFCM-MRF model in noisy image
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Region-based segmentation of 2D and 3D images with tissue-like P systems
Pattern Recognition Letters
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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.