Robust fuzzy clustering-based image segmentation |
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| Authors: | Zhang Yang Fu-Lai Chung Wang Shitong |
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| Affiliation: | 1. School of Information, Southern Yangtze University, WuXi, JiangSu, China;2. Department of Computing, Hong Kong Polytechnic University, Hong Kong, China;1. Department of Radiology, University of Pittsburgh, 3362 5th Avenue, Room 104, Pittsburgh, PA 15213, United States;2. School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore;3. Institute of Mental Health, Woodbridge Hospital, 10 Buangkok View, Singapore 539747, Singapore;1. Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland;2. Department of Computer Medical Systems, Institute of Medical Technology and Equipment, Roosevelt St. 118, 41-800 Zabrze, Poland;1. Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science, Northwestern Polytechnical University, Xi?an 710072, China;2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;1. School of Materials and Physics, China University of Mining and Technology, Xuzhou, 221116, PR China;2. School of Material Science and Engineering, Jiangsu University, Zhenjiang, Jiangsu, PR China |
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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. |
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