FCM Clustering Segmentation Algorithms Based on Spatial Constraint

Abstract:

In order to improve the performance of FCM clustering segmentation algorithm, a new spatial constrained FCM (SCFCM) algorithm is proposed. The new algorithm computes the membership degree of each pixel according to membership degrees of its neighbor pixels. For the purpose of enhance compute efficiency, two kinds of initial membership matrix creation algorithms are proposed to reduce iteration times. Experiments for a series of images are performed, according to the results, SCFCM clustering segmentation algorithm can restrain the noise effectively.

• Periodical: Advanced Materials Research (Volume 411)
• Pages: 497-500
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.411.497
• Online since: November 2011
• Authors: Jun Wei Tian, Ya Lin Yu, Tao Shen
• Keywords: Fuzzy C-Means Clustering (FCM), Image Segmentation, Robustness, Spatial Constrained Fuzzy C-Means Clustering (SCFCM)

References

[1] J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York, 1981.

[2] G.C. Karmakar, L.S. Dooley, A generic fuzzy rule based image segmentation algorithm, Pattern Recognition Lett. 23 (10) (2002) 1215–1227.

DOI: 10.1016/s0167-8655(02)00069-7

[3] Y.A. Tolias, S.M. Panas, Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions, IEEE Trans. Systems Man Cybernet. A 28 (1998) 359–369.

DOI: 10.1109/3468.668967

[4] D.L. Pham, Fuzzy clustering with spatial constraints, in: IEEE Proceedings of the International Conference Image Processing, New York, 2002, pp. II-65–II-68.

[5] M.N. Ahmed, S.M. Yamany, N. Mohamed, A.A. Farag, T. Moriarty, A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data, IEEE Trans. Med. Imaging 21 (2002) 193–199.

DOI: 10.1109/42.996338

[6] A.W.C. Liew, H. Yan, An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation, IEEE Trans Med Imag, 2003,22:1063–1075.

DOI: 10.1109/tmi.2003.816956

[7] K.S. Chuang, H.L. Tzeng, S, Chen, J. Wu, T.J Chen, Fuzzy c-means clustering with spatial information for image segmentation, Computerized Medical Imaging and Graphics, 2006, 30: 9–15.

DOI: 10.1016/j.compmedimag.2005.10.001

[8] Jajuga K. L norm based fuzzy clustering, Fuzzy Sets Syst., 1991, 39(1): 43–50.

DOI: 10.1016/0165-0114(91)90064-w

[9] R.J. Hathaway, J.C. Bezdek, Generalized fuzzy c-means clustering strategies using L norm distance, IEEE Trans Fuzzy Syst, 2000, 8: 576–572.

DOI: 10.1109/91.873580

[10] J. Leski, An insensitive approach to fuzzy clustering [J]. International Journal of Applicat Math Comp Sci, 2001,11(4):993–1007.

[11] D.L. Pham, J.L. Prince, Adaptive fuzzy segmentation of magnetic resonance images, IEEE Trans Med Imag, 1999, 18:737-752.

DOI: 10.1109/42.802752

[12] H. Renjie, S. Datta, B.R. Sajja, et al, Adaptive FCM with contextual constrains for segmentation of multi-spectral MRI, in: Proceedings of the 26th Annual International Conference of the IEEE EMBS. 2004:1660-1663.

DOI: 10.1109/iembs.2004.1403501

[13] S.C. Chen, D.Q. Zhang, Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure, IEEE Trans. Systems Man Cybernet. B 34 (4) (2004) 1907–1916.

DOI: 10.1109/tsmcb.2004.831165

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

DOI: 10.1016/j.patcog.2006.07.011