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

Alan Wee-Chung Liew·Hong Yan
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Abstract

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.

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AlgorithmsAnatomy, Cross-SectionalBrainCluster AnalysisComputer SimulationFeedbackFuzzy LogicHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificityEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tValidation Study

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BibTeXRIS

Alan Wee-Chung Liew, Hong Yan. 2003. An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation.. https://doi.org/10.1109/tmi.2003.816956

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