A robust fuzzy clustering algorithm using mean-field-approximation based hidden Markov random field model for image segmentation

• Published in: Journal of intelligent & fuzzy systems Vol. 32; no. 1; pp. 177 - 188
• Main Authors: Chen, Aiguo, Wang, Shitong
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2017; Sage Publications Ltd
• Subjects: Algorithms; Clustering; Fields (mathematics); Image segmentation; Markov analysis; Markov chains; Robustness (mathematics); Spatial data; mean field approximation; Image segmentation; fuzzy c-means clustering; hidden Markov random field
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-151345

Although how to deal well with images corrupted with noise is a commonly encountered task in image segmentation, the design of efficient and robust segmentation algorithms still keeps a challenging research topic. In this paper, a robust fuzzy-clustering-based image segmentation algorithm is presented to effectively segment noisy images. The proposed algorithm is derived from both the conventional fuzzy c-means (FCM) clustering algorithm and the hidden Markov random field (HMRF) model with the capability of incorporating spatial information. The performance of the proposed algorithm is experimentally evaluated with the comparison algorithms. Experimental results on synthetic and real images demonstrate the effectiveness of the proposed algorithm.