Laser & Optoelectronics Progress, Volume. 59, Issue 22, 2210001(2022)

Fast and Automatic Fuzzy C-Means Clustering Color Image Segmentation Algorithm

Chao Wang1、*, Yongshun Wang1, and Fan Di2
Author Affiliations
  • 1School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu , China
  • 2Diaoyutai Hotel Administration, Ministry of Foreign Affairs, Beijing 100080, China
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    References(20)

    [1] Wang J, Zhang X Y, Cai Y F et al. CT image segmentation method combining wavelet transform and RSF model[J]. Acta Optica Sinica, 40, 2110003(2020).

    [2] Wang Y T, Li Q. Terahertz holographic reconstructed image segmentation based on optimized region growth by evolutionary algorithm[J]. Chinese Journal of Lasers, 47, 0814003(2020).

    [3] Feng B, Chen Y H, Liu Z S et al. Segmentation of breast cancer on DCE-MRI images with MRF energy and fuzzy speed function[J]. Acta Automatica Sinica, 46, 1188-1199(2020).

    [4] Mu H W, Guo Y, Quan X H et al. Magnetic resonance imaging brain tumor image segmentation based on improved U-net[J]. Laser & Optoelectronics Progress, 58, 0410022(2021).

    [8] Szilagyi L, Benyo Z, Szilagyi S M et al. MR brain image segmentation using an enhanced fuzzy C-means algorithm[C], 724-726(2003).

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    Chao Wang, Yongshun Wang, Fan Di. Fast and Automatic Fuzzy C-Means Clustering Color Image Segmentation Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2210001

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    Paper Information

    Category: Image Processing

    Received: Aug. 13, 2021

    Accepted: Sep. 24, 2021

    Published Online: Sep. 23, 2022

    The Author Email: Chao Wang (18334704680@163.com)

    DOI:10.3788/LOP202259.2210001

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