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Unsupervised Image Segmentation Using Penalized Fuzzy Clustering Algorithm

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Intelligent Data Engineering and Automated Learning - IDEAL 2005 (IDEAL 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3578))

Abstract

Fuzzy c-means (FCM) clustering algorithm as an unsupervised fuzzy clustering technique has been widely used in image segmentation. However, the conventional FCM algorithm is very sensitive to noise for the reason of incorporating no information about spatial context while segmentation. To overcome this limitation of FCM algorithm, a novel penalized fuzzy c-means (PFCM) algorithm for image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the original FCM algorithm by a penalty term, which is employed to take into account the spatial dependence of the objects. Experiments demonstrate the proposed algorithm is effective and more robust to noise and other artifacts than the standard FCM algorithm.

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Authors and Affiliations

  1. Key Laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an, 710049, P.R. China

    Yong Yang, Feng Zhang, Chongxun Zheng & Pan Lin

Authors
  1. Yong Yang
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  2. Feng Zhang
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  3. Chongxun Zheng
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  4. Pan Lin
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Editor information

Editors and Affiliations

  1. School of Information Technology and Electrical Engineering, University of Queensland, 4072, Australia

    Marcus Gallagher

  2.  , POB 30031, FL 32503-1031, Pensacola

    James P. Hogan

  3. Faculty of Information Technology, Queensland University of Technology, Box 2434, Q 4001, Brisbane, Australia

    Frederic Maire

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© 2005 Springer-Verlag Berlin Heidelberg

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Yang, Y., Zhang, F., Zheng, C., Lin, P. (2005). Unsupervised Image Segmentation Using Penalized Fuzzy Clustering Algorithm. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_10

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  • DOI: https://doi.org/10.1007/11508069_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26972-4

  • Online ISBN: 978-3-540-31693-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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