Improved Fuzzy C-Means Algorithm for Image Segmentation

Xuegang Hu; Lei Li

doi:doi:10.11648/j.jeee.20150301.11

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Improved Fuzzy C-Means Algorithm for Image Segmentation

Xuegang Hu, Lei Li

Published in Journal of Electrical and Electronic Engineering (Volume 3, Issue 1)

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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	X. Muñoz, J. Freixenet, X. Cufı, et al, “Strategies for image segmentation combining region and boundary information,” Pattern recognition letters vol. 24, no. 1, pp. 375-392.
[2]	J. Dunn, “A fuzzy relative of the ISO-DATA process and its use in detecting compact well separated clusters,” J. Cybern., vol. 3, no. 3, pp. 32-57, 1974.
[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.
[10]	S. Krinidis and V. Chatzis, “A robust fuzzy local information C-means clustering algorithm,” IEEE Trans. Image Process., vol. 19, no. 5, pp. 1328-1337, May 2010.
[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.
[12]	M. Gong, Z. Zhou, J. Ma, “Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering,” IEEE Trans. Image Process, vol. 21, no. 4, pp. 2141-2151, 2012.
[13]	M. Gong, Y. Liang, J. Shi, et al, “Fuzzy c-means clustering with local information and kernel metric for image segmentation,” IEEE Trans. Image Process, vol. 22, no. 2, pp. 573-584, 2013.
[14]	Pal, R. Nikhil and C. James, “On cluster validity for the fuzzy c-means model,” IEEE Trans. Fuzzy Syst. vol. 3, no. 3, pp. 370-379, 1995.

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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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TY  - JOUR
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

Xuegang Hu

College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China; Research Center of System Science, Chongqing University of Posts and Telecommunications, Chongqing, China
Lei Li

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

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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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
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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