• Laser & Optoelectronics Progress
  • Vol. 57, Issue 2, 21001 (2020)
Zhao Zhanmin1、2, Zhu Zhanlong1、2、*, Liu Yongjun1, Liu Ming1、2, and Zheng Yibo2
Author Affiliations
  • 1School of Information Engineering, Heibei GEO University, Shijiazhuang, Hebei 0 50031, China
  • 2Hebei Key Laboratory of Optoelectronic Information and Geo-Detection Technology, Heibei GEO University, Shijiazhuang, Hebei 0 50031, China
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    DOI: 10.3788/LOP57.021001 Cite this Article Set citation alerts
    Zhao Zhanmin, Zhu Zhanlong, Liu Yongjun, Liu Ming, Zheng Yibo. Fuzzy C-Means Clustering Algorithm for Image Segmentation Insensitive to Cluster Size[J]. Laser & Optoelectronics Progress, 2020, 57(2): 21001 Copy Citation Text show less
    References

    [1] Zhang X F, Wang G, Su Q T et al. An improved fuzzy algorithm for image segmentation using peak detection, spatial information and reallocation[J]. Soft Computing, 21, 2165-2173(2017).

    [2] Nie F Y, Li J Q, Zhang P F et al. A threshold selection method for image segmentation based on Tsallis relative entropy[J]. Laser & Optoelectronics Progress, 54, 071002(2017).

    [3] Tang R Y, Liu D A, Zhu J Q. Micro-size damage adaptive detection technology based on local signal-to-noise ratio[J]. Chinese Journal of Lasers, 45, 0704001(2018).

    [4] Ahmed M N, Yamany S M, Mohamed N et al. A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data[J]. IEEE Transactions on Medical Imaging, 21, 193-199(2002).

    [5] Guo F F, Wang X X, Shen J. Adaptive fuzzy C-means algorithm based on local noise detecting for image segmentation[J]. IET Image Processing, 10, 272-279(2016).

    [6] Zhang Y, Huang D, Ji M et al. Image segmentation using PSO and PCM with Mahalanobis distance[J]. Expert Systems With Applications, 38, 9036-9040(2011).

    [7] Zhu Z L, Wang J F. Image segmentation based on adaptive fuzzy C-means and post processing correction[J]. Laser & Optoelectronics Progress, 55, 011004(2018).

    [8] Huang H, Jin Y Y, Li Z Y et al. Fluorescent microsphere segmentation and classification based on watershed and semi-supervised minor reconstruction error[J]. Chinese Journal of Lasers, 45, 0307013(2018).

    [9] Bezdek J C. A convergence theorem for the fuzzy ISODATA clustering algorithms[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2, 1-8(1980).

    [10] Li T T, Jiang Z H, Rao Y et al. Image segmentation based on gene expression programming and spatial fuzzy clustering[J]. Journal of Image and Graphics, 22, 575-583(2017).

    [11] Krinidis S, Chatzis V. A robust fuzzy local information C-means clustering algorithm[J]. IEEE Transactions on Image Processing, 19, 1328-1337(2010).

    [12] Celik T, Lee H K. Comments on “a robust fuzzy local information C-means clustering algorithm”[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 22, 1258-1261(2013).

    [13] Chen S C, Zhang D Q. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J]. IEEE Transactions on Systems, Man, and Cybernetics. Part b, Cybernetics: a Publication of the IEEE Systems, Man, and Cybernetics Society, 34, 1907-1916(2004).

    [14] Szilágyi L, Benyó Z, Szilágyi S M et al. MR brain image segmentation using an enhanced fuzzy C-means algorithm. [C]∥Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, September 17-21, 2003, Cancun, Mexico. New York: IEEE, 724-726(2003).

    [15] Cai W L, Chen S C, Zhang D Q. Fast and robust fuzzy C-means clustering algorithms incorporating local information for image segmentation[J]. Pattern Recognition, 40, 825-838(2007).

    [16] Noordam J C, Buydens L M C. Multivariate image segmentation with cluster size insensitive fuzzy C-means[J]. Chemometrics and Intelligent Laboratory Systems, 64, 65-78(2002).

    [17] Ji Z X, Sun Q S, Xia D S. A modified possibilistic fuzzy C-means clustering algorithm for bias field estimation and segmentation of brain MR image[J]. Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society, 35, 383-397(2011).

    [18] Zhao F, Fan J L, Liu H Q. Optimal-selection-based suppressed fuzzy C-means clustering algorithm with self-tuning non local spatial information for image segmentation[J]. Expert Systems with Applications, 41, 4083-4093(2014).

    [19] Zhao F. Fuzzy clustering algorithms with self-tuning non-local spatial information for image segmentation[J]. Neurocomputing, 106, 115-125(2013).

    [20] Wen C J, Zhan Y Z, Ke J. General equalization fuzzy C-means clustering algorithm[J]. Systems Engineering-Theory & Practice, 32, 2751-2755(2012).

    [21] Liu Y, Hou T, Liu F. Improving fuzzy C-means method for unbalanced dataset[J]. Electronics Letters, 51, 1880-1882(2015).

    [22] Mukhopadhyay A, Maulik U. A multiobjective approach to MR brain image segmentation[J]. Applied Soft Computing, 11, 872-880(2011).

    Zhao Zhanmin, Zhu Zhanlong, Liu Yongjun, Liu Ming, Zheng Yibo. Fuzzy C-Means Clustering Algorithm for Image Segmentation Insensitive to Cluster Size[J]. Laser & Optoelectronics Progress, 2020, 57(2): 21001
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