Fuzzy c-means clustering with non local spatial information for noisy image segmentation
- 327 Downloads
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 cmeans 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.
Keywordsimage segmentation fuzzy clustering algorithm non local spatial information magnetic resonance (MR) image
Unable to display preview. Download preview PDF.
- 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)Google Scholar
- 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, 724–726Google Scholar
- 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, 60–65Google Scholar
- 15.Wu M, Schölkopf B. A local learning approach for clustering. In: Proceedings of 20th Annual Conference on Neural Information Processing Systems. 2007, 1529–1536Google Scholar
- 17.Bezdek J C. Mathematical models for systematic and taxonomy. In: Proceedings of 8th International Conference on Numerical Taxonomy. 1975, 143–166Google Scholar