| Peer-Reviewed

Improved Fuzzy C-Means Algorithm for Image Segmentation

Received: 26 December 2014     Accepted: 8 January 2015     Published: 22 January 2015
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Abstract

In order to preserve more image details and enhance its robustness to noise for image segmentation, an improved fuzzy c-means algorithm (FCM) for image segmentation is presented by incorporating the local spatial information and gray level information in this paper. The modified membership function and clustering center function are more mathematically reasonable than those of the FLICM, so the iterative sequence can converge to a local minimum value of the improved objective function. The new fuzzy factor grants the algorithm a novel balance between robustness to noise and effectiveness of preserving the details. The revised algorithm flow has significantly accelerated the processing procedure. Through these improvements, the experiments on the artificial and real images show that the proposed algorithm is very effective.

Published in Journal of Electrical and Electronic Engineering (Volume 3, Issue 1)
DOI 10.11648/j.jeee.20150301.11
Page(s) 1-5
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Clustering, Image Segmentation, Fuzzy C-Means, Local Minimum Value, Gray Level Information

References
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[3] J. Bezdek, “Pattern recognition with fuzzy objective function algorithms,” New York: Plenum, 1981.
[4] Y. Liu, X. Wang, H. Yu, W. Zhang, “Brain tumor segmentation based on morphological multiscale modification and fuzzy c-means clustering,” Journal of Computer Applications, vol. 34, no. 9, pp. 2711-2715, 2014.
[5] M. Ahmed, S. Yamany, N. Mohamed, et al, “A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data,” IEEE Trans. Med. Imag., vol. 21, no. 3, pp. 193-199, 2002.
[6] Y. Tolias and S. Panas, “Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions,” IEEE Trans. Syst., Man, Cybern., vol. 28, no. 3, pp. 359-369, Mar. 1998.
[7] D. Pham, “Fuzzy clustering with spatial constraints,” in Proc. Int. Conf. Image Processing. New Work, 2002, vol. Ⅱ, pp. 65-68.
[8] S. Chen, D. Zhang, “Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure,” IEEE Trans. Syst., Man, Cybern., vol. 34, pp. 1907-1916, 2004.
[9] W. Cai, S. Chen, D. Zhang, “Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation,” Pattern Recognition, vol. 40, no. 3, pp. 825-838, Mar. 2007.
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[11] T. Celik and H. Lee, “Comments on “A Robust Fuzzy Local Information C-Means Clustering Algorithm”,” IEEE Trans. Image Process, vol. 22, no. 3, pp. 1258-1261, 2013.
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  • APA Style

    Xuegang Hu, Lei Li. (2015). Improved Fuzzy C-Means Algorithm for Image Segmentation. Journal of Electrical and Electronic Engineering, 3(1), 1-5. https://doi.org/10.11648/j.jeee.20150301.11

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    ACS Style

    Xuegang Hu; Lei Li. Improved Fuzzy C-Means Algorithm for Image Segmentation. J. Electr. Electron. Eng. 2015, 3(1), 1-5. doi: 10.11648/j.jeee.20150301.11

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    AMA Style

    Xuegang Hu, Lei Li. Improved Fuzzy C-Means Algorithm for Image Segmentation. J Electr Electron Eng. 2015;3(1):1-5. doi: 10.11648/j.jeee.20150301.11

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  • @article{10.11648/j.jeee.20150301.11,
      author = {Xuegang Hu and Lei Li},
      title = {Improved Fuzzy C-Means Algorithm for Image Segmentation},
      journal = {Journal of Electrical and Electronic Engineering},
      volume = {3},
      number = {1},
      pages = {1-5},
      doi = {10.11648/j.jeee.20150301.11},
      url = {https://doi.org/10.11648/j.jeee.20150301.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20150301.11},
      abstract = {In order to preserve more image details and enhance its robustness to noise for image segmentation, an improved fuzzy c-means algorithm (FCM) for image segmentation is presented by incorporating the local spatial information and gray level information in this paper. The modified membership function and clustering center function are more mathematically reasonable than those of the FLICM, so the iterative sequence can converge to a local minimum value of the improved objective function. The new fuzzy factor grants the algorithm a novel balance between robustness to noise and effectiveness of preserving the details. The revised algorithm flow has significantly accelerated the processing procedure. Through these improvements, the experiments on the artificial and real images show that the proposed algorithm is very effective.},
     year = {2015}
    }
    

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    T1  - Improved Fuzzy C-Means Algorithm for Image Segmentation
    AU  - Xuegang Hu
    AU  - Lei Li
    Y1  - 2015/01/22
    PY  - 2015
    N1  - https://doi.org/10.11648/j.jeee.20150301.11
    DO  - 10.11648/j.jeee.20150301.11
    T2  - Journal of Electrical and Electronic Engineering
    JF  - Journal of Electrical and Electronic Engineering
    JO  - Journal of Electrical and Electronic Engineering
    SP  - 1
    EP  - 5
    PB  - Science Publishing Group
    SN  - 2329-1605
    UR  - https://doi.org/10.11648/j.jeee.20150301.11
    AB  - In order to preserve more image details and enhance its robustness to noise for image segmentation, an improved fuzzy c-means algorithm (FCM) for image segmentation is presented by incorporating the local spatial information and gray level information in this paper. The modified membership function and clustering center function are more mathematically reasonable than those of the FLICM, so the iterative sequence can converge to a local minimum value of the improved objective function. The new fuzzy factor grants the algorithm a novel balance between robustness to noise and effectiveness of preserving the details. The revised algorithm flow has significantly accelerated the processing procedure. Through these improvements, the experiments on the artificial and real images show that the proposed algorithm is very effective.
    VL  - 3
    IS  - 1
    ER  - 

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Author Information
  • College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

  • College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

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