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Image Guided Fuzzy C-Means for Image Segmentation

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Abstract

Prior knowledge has been considered as valuable supplementary information in many image processing techniques. In this paper, we take the input image itself as the guidance prior and develop a novel fuzzy clustering algorithm to segment it by adding a new term to the objective function of Fuzzy C-Means. The new term comes from Guided Filter for its capability in noise suppression and edge-preserving smoothing. As a result, the memberships derived from the new objective function incorporate the guidance information from the image to be segmented. In this way, the segmentation result retains more subtle details on the boundaries of segments. According to experimental results, the proposed method shows good performance in image segmentation tasks especially for images with high noise rates.

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Acknowledgements

This paper is partially supported by University of Macau RC MYRG2015-00148-FST, Science and Technology Development Fund, Macao S.A.R (067/2014/A, 097/2015/A3) and National Nature Science Foundation of China under Grant No.: 61673405.

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Authors and Affiliations

  1. Department of Computer and Information Science, University of Macau, Avenida da Universidade, Taipa, Macau, China

    Li Guo, Long Chen, Yingwen Wu & C. L. Philip Chen

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  1. Li Guo
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  2. Long Chen
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  3. Yingwen Wu
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  4. C. L. Philip Chen
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Correspondence to Long Chen.

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Guo, L., Chen, L., Wu, Y. et al. Image Guided Fuzzy C-Means for Image Segmentation. Int. J. Fuzzy Syst. 19, 1660–1669 (2017). https://doi.org/10.1007/s40815-017-0322-1

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  • DOI: https://doi.org/10.1007/s40815-017-0322-1

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