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

Article type: Research Article

Authors: Chen, Aiguo^* | Wang, Shitong

Affiliations: School of Digital Media, Jiangnan University, Wuxi, Jiangsu, P.R. China

Correspondence: [*] Corresponding author. Aiguo Chen, School of Digital Media, Jiangnan University, Wuxi, Jiangsu, P.R. China. Tel.: +86 510 85915666; Fax: +86 510 85913570; E-mail: agchen@jiangnan.edu.cn.

Abstract: 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.

Keywords: Image segmentation, fuzzy c-means clustering, hidden Markov random field, mean field approximation

DOI: 10.3233/JIFS-151345

Journal: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 1, pp. 177-188, 2017