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Intuitionistic fuzzy \(c\)-means clustering algorithm with neighborhood attraction in segmenting medical image

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Abstract

Fuzzy segmentation methods, especially fuzzy \(c\)-means algorithms, have been widely used in medical imaging in past decades. This paper proposes a novel neighborhood intuitionistic fuzzy \(c\)-means clustering algorithm with a genetic algorithm (NIFCMGA). This new clustering algorithm technology can retain the advantages of an intuitionistic fuzzy \(c\)-means clustering algorithm to maximize benefits and reduce noise/outlier influences through neighborhood membership. Furthermore, the genetic algorithms were used simultaneously to select the optimal parameters of the proposed clustering algorithm. This proposed technology has been successfully applied to the clustering of different regions of magnetic resonance imaging and computerized tomography scanning, which may be extended to the diagnosis of abnormalities. Comparisons with other approaches demonstrate the superior performance of the proposed NIFCMGA.

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Acknowledgments

The authors would like to thank National Science Council of the Republic of China for financially supporting this research under Contract No. NSC 102-2410-H-262-008.

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Authors and Affiliations

  1. Department of Diagnostic Radiology, Shin Kong Wu Ho-Su Memorial Hospital, Shihlin, Taipei, 111, Taiwan

    Ching-Wen Huang

  2. Department of Information Management, Lunghwa University of Science and Technology, Taoyuan, 333, Taiwan

    Kuo-** Lin, Ming-Chang Wu, Gia-Shie Liu & Chih-Hung Jen

  3. Department of Computer Science and Information Management, Hungkuang University, Taichung, 433, Taiwan

    Kuo-Chen Hung

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  1. Ching-Wen Huang
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  2. Kuo-** Lin
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  3. Ming-Chang Wu
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  4. Kuo-Chen Hung
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  5. Gia-Shie Liu
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  6. Chih-Hung Jen
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Corresponding author

Correspondence to Kuo-** Lin.

Additional information

Communicated by T.-P. Hong.

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Huang, CW., Lin, KP., Wu, MC. et al. Intuitionistic fuzzy \(c\)-means clustering algorithm with neighborhood attraction in segmenting medical image. Soft Comput 19, 459–470 (2015). https://doi.org/10.1007/s00500-014-1264-2

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