[2] [2] 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, 2016, 10(4): 272-279.

[3] [3] KRINIDIS S, CHATZIS V. A robust fuzzy slocal information C-means clustering algorithm[J]. IEEE Transactions on Image Processing, 2010, 19 (5): 1328-1337.

[4] [4] 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, 2014, 41(9): 4083-4093.

[5] [5] SZILAGYI L, BENYO Z, SZILAGYI 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. Cancun: IEEE, 2003: 724-726.

[6] [6] 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, 2007, 40(3): 825-838.

[10] [10] NOORDAM J C, VAN DEN BROEK W H A M, BUYDENS L M C. Multivariate image segmentation with cluster size insensitive fuzzy C-means[J]. Chemometrics and Intelligent Laboratory Systems, 2002, 64(1): 65-78.

[11] [11] LIU Y, HOU T, LIU F. Improving fuzzy c-means method for unbalanced dataset[J]. Electronics Letters, 2015, 51(23): 1880-1882.

[12] [12] GENG X, ZHAN D C, ZHOU Z H, et al. Supervised nonlinear dimensionality reduction for visualization and classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2005, 35(6): 1098-1107.