Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12282
Title: Robust fuzzy clustering-based image segmentation
Authors: Zhang, Y
Chung, FL 
Wang, S
Keywords: Fuzzy clustering
Image segmentation
The objective function
Issue Date: 2009
Publisher: Elsevier
Source: Applied soft computing, 2009, v. 9, no. 1, p. 80-84 How to cite?
Journal: Applied soft computing 
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.
URI: http://hdl.handle.net/10397/12282
ISSN: 1568-4946
EISSN: 1872-9681
DOI: 10.1016/j.asoc.2008.03.009
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