A generalized spatial fuzzy C-means algorithm for medical image segmentation
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Two-dimensional clustering algorithms for image segmentation
WSEAS Transactions on Computers
A learning based self-organized additive fuzzy clustering method and its application for EEG data
International Journal of Knowledge-based and Intelligent Engineering Systems - Intelligent Information Processing: Techniques and Applications
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Abstract: An adaptive fuzzy clustering algorithm is presented for the fuzzy segmentation of medical images. By using a novel dissimilarity index in the cost functional of the fuzzy clustering algorithm, our algorithm is capable of utilising contextual information in a 3 脳 3 neighborhood to impose local spatial homogenuity, as well as the usual feature space homogenuity. This has the effects of smoothing out random noise and resolving classification ambiguities. By introducing a multiplicative bias field into the cost functional, artifacts due to smooth, non-uniform intensity variation can also be corrected. The bias field is regularized by a Laplacian term which forces the bias field to resist bending and to be smooth. To solve for the bias field, the Full Multigrid Algorithm is employed. Experimental results on a synthetic image and a simulated MRI brain image with noise and non-uniform intensity variation have illustrated the effectiveness of the proposed algorithm.