Robust fuzzy clustering-based image segmentation

Authors:
Zhang Yang;Fu-Lai Chung;Wang Shitong
Affiliations:
School of Information, Southern Yangtze University, WuXi, JiangSu, China;Department of Computing, Hong Kong Polytechnic University, Hong Kong, China;School of Information, Southern Yangtze University, WuXi, JiangSu, China and Department of Computing, Hong Kong Polytechnic University, Hong Kong, China
Venue:
Applied Soft Computing
Year:
2009

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Abstract

The fuzzy clustering algorithm fuzzy c-means (FCM) is often used for image segmentation. When noisy image segmentation is required, FCM should be modified such that it can be less sensitive to noise in an image. In this correspondence, a robust fuzzy clustering-based segmentation method for noisy images is developed. The contribution of the study here is twofold: (1) we derive a robust modified FCM in the sense of a novel objective function. The proposed modified FCM here is proved to be equivalent to the modified FCM given by Hoppner and Klawonn [F. Hoppner, F. Klawonn, Improved fuzzy partitions for fuzzy regression models, Int. J. Approx. Reason. 32 (2) (2003) 85-102]. (2) We explore the very applicability of the proposed modified FCM for noisy image segmentation. Our experimental results indicate that the proposed modified FCM here is very suitable for noisy image segmentation.