Fuzzy c-means clustering with non local spatial information for noisy image segmentation

Abstract

As an effective image segmentation method, the standard fuzzy c-means (FCM) clustering algorithm is very sensitive to noise in images. Several modified FCM algorithms, using local spatial information, can overcome this problem to some degree. However, when the noise level in the image is high, these algorithms still cannot obtain satisfactory segmentation performance. In this paper, we introduce a non local spatial constraint term into the objective function of FCM and propose a fuzzy c-means clustering algorithm with non local spatial information (FCM_NLS). FCM_NLS can deal more effectively with the image noise and preserve geometrical edges in the image. Performance evaluation experiments on synthetic and real images, especially magnetic resonance (MR) images, show that FCM_NLS is more robust than both the standard FCM and the modified FCM algorithms using local spatial information for noisy image segmentation.

Keywords

image segmentation / fuzzy clustering algorithm / non local spatial information / magnetic resonance (MR) image

References

[1] Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66

[2] Baradez M O, McGuckin C P, Forraz N, Pettengellc R, Hoppe A. Robust and automated unimodal histogram thresholding and potential applications. Pattern Recognition, 2004, 37(6): 1131-1148

[3] Pappas T N. An adaptive clustering algorithm for image segmentation. IEEE Transactions on Signal Processing, 1992, 40(4): 901-914

[4] Cinque L, Foresti G, Lombardi L. A clustering fuzzy approach for image segmentation. Pattern Recognition, 2004, 37(9): 1797-1807

[5] Chen S C, Zhang D Q. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans System, Man, and Cybernetics. Part B: Cybernetics, 2004, 34(4): 1907-1916

[6] Caldairou B, Passat N, Habas P A, Studholme C, Rousseau F. A non-local fuzzy segmentation method: application to brain MRI. Pattern Recognition, 2010 (in press)

[7] Hojjatoleslami S A, Kittler J. Region growing: a new approach. IEEE Trans Image Processing, 1998, 7(7): 1079-1084

[8] Osher S, Paragios N. Geometric Level Set Methods in Imaging, Vision, and Graphics. New York: Springer-Verlag, 2003

[9] Bezdek J C. Pattern Recognition With Fuzzy Objective Function Algorithms. New York: Plenum Press, 1981

[10] Ahmed M N, Yamany S M, Mohamed N, Farag A A, Moriarty T. A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transactions on Medical Imaging, 2002, 21(3): 193-199

[11] Zhang D Q, Chen S C. A novel kernelized fuzzy C-means algorithm with application in medical image segmentation. Artificial Intelligence in Medicine, 2004, 32(1): 37-50

[12] Szilágyi L, Benyo Z, Szilágyi S, Adam H S. MR brain image segmentation using an enhanced fuzzy C-means algorithm. In: Proceedings of 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2003, 724-726

[13] Cai W, Chen S, Zhang D. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition, 2007, 40(3): 825-838

[14] Buades A, Coll B, Morel J M. A non-local algorithm for image denoising. In: Procceeding of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005, 60-65

[15] Wu M, Schölkopf B. A local learning approach for clustering. In: Proceedings of 20th Annual Conference on Neural Information Processing Systems. 2007, 1529-1536

[16] Bezdek J C. Cluster validity with fuzzy sets. Cybernetics and Systems, 1973, 3(3): 58-73

[17] Bezdek J C. Mathematical models for systematic and taxonomy. In: Proceedings of 8th International Conference on Numerical Taxonomy. 1975, 143-166

Acknowledgement

The authors would like to thank the anonymous reviewers for their detailed reviews and constructive comments. This work was supported by the National Natural Science Foundation of China (Grant Nos. 60702062, 60970067), the National High Technology Research and Development Program (863 Program) (2008AA01Z125, 2009AA12Z210), and the Fund for Foreign Scholars in University Research and Teaching Programs (111 Project) (B07048).

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg