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An adaptive fuzzy clustering algorithm for medical image segmentation

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2 Author(s)
Liew, A.W.-C. ; Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, China ; Hong Yan

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 3×3 neighborhood to impose local spatial homogeneity, as well as the usual feature space homogeneity. 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

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Medical Imaging and Augmented Reality, 2001. Proceedings. International Workshop on

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