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New two-dimensional fuzzy C-means clustering algorithm for image segmentation

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Abstract

To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this method, the image segmentation was converted into an optimization problem. The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixels described by the improved two-dimensional histogram. By making use of the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center could be obtained by iterative optimization, and the image segmentation could be accomplished. The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%. The proposed algorithm has strong anti-noise capability, high clustering accuracy and good segment effect, indicating that it is an effective algorithm for image segmentation.

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Xian-cheng Zhou  (周鲜成), Qun-tai Shen  (申群太) & Li-mei Liu  (刘利枚)

  2. School of Computer and Electronic Engineering, Hunan University of Commerce, Changsha, 410205, China

    Xian-cheng Zhou  (周鲜成) & Li-mei Liu  (刘利枚)

Authors
  1. Xian-cheng Zhou  (周鲜成)
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  2. Qun-tai Shen  (申群太)
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  3. Li-mei Liu  (刘利枚)
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Corresponding author

Correspondence to Xian-cheng Zhou  (周鲜成).

Additional information

Foundation item: Project(06JJ50110) supported by the Natural Science Foundation of Hunan Province, China

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Zhou, Xc., Shen, Qt. & Liu, Lm. New two-dimensional fuzzy C-means clustering algorithm for image segmentation. J. Cent. South Univ. Technol. 15, 882–887 (2008). https://doi.org/10.1007/s11771-008-0161-1

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  • DOI: https://doi.org/10.1007/s11771-008-0161-1

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