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An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation

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2 Author(s)
A. W. C. Liew ; Dept. of Comput. Eng. & Inf. Technol., City Univ. of Hong Kong, University Of Sydney, NSW, Australia ; Hong Yan

An adaptive spatial fuzzy c-means clustering algorithm is presented in this paper for the segmentation of three-dimensional (3-D) magnetic resonance (MR) images. The input images may be corrupted by noise and intensity nonuniformity (INU) artifact. The proposed algorithm takes into account the spatial continuity constraints by using a dissimilarity index that allows spatial interactions between image voxels. The local spatial continuity constraint reduces the noise effect and the classification ambiguity. The INU artifact is formulated as a multiplicative bias field affecting the true MR imaging signal. By modeling the log bias field as a stack of smoothing B-spline surfaces, with continuity enforced across slices, the computation of the 3-D bias field reduces to that of finding the B-spline coefficients, which can be obtained using a computationally efficient two-stage algorithm. The efficacy of the proposed algorithm is demonstrated by extensive segmentation experiments using both simulated and real MR images and by comparison with other published algorithms.

Published in:

IEEE Transactions on Medical Imaging  (Volume:22 ,  Issue: 9 )