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An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation.
IEEE Trans Med Imaging. 2003 Sep; 22(9):1063-75.IT

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.

Authors+Show Affiliations

Liew AW
Department of Computer Engineering and Information Technology, City University of Hong Kong, Hong Kong. itwcliew@cityu.edu.hk
Yan H
No affiliation info available

MeSH

AlgorithmsAnatomy, Cross-SectionalBrainCluster AnalysisComputer SimulationFeedbackFuzzy LogicHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingPattern Recognition, AutomatedReproducibility of ResultsSensitivity and Specificity

Pub Type(s)

Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't
Validation Study

Language

eng

PubMed ID

12956262

Citation

Liew, Alan Wee-Chung, and Hong Yan. "An Adaptive Spatial Fuzzy Clustering Algorithm for 3-D MR Image Segmentation." IEEE Transactions On Medical Imaging, vol. 22, no. 9, 2003, pp. 1063-75.
Liew AW, Yan H. An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. IEEE Trans Med Imaging. 2003;22(9):1063-75.
Liew, A. W., & Yan, H. (2003). An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. IEEE Transactions On Medical Imaging, 22(9), 1063-75.
Liew AW, Yan H. An Adaptive Spatial Fuzzy Clustering Algorithm for 3-D MR Image Segmentation. IEEE Trans Med Imaging. 2003;22(9):1063-75. PubMed PMID: 12956262.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. AU - Liew,Alan Wee-Chung, AU - Yan,Hong, PY - 2003/9/6/pubmed PY - 2004/1/30/medline PY - 2003/9/6/entrez SP - 1063 EP - 75 JF - IEEE transactions on medical imaging JO - IEEE Trans Med Imaging VL - 22 IS - 9 N2 - 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. SN - 0278-0062 UR - https://www.unboundmedicine.com/medline/citation/12956262/An_adaptive_spatial_fuzzy_clustering_algorithm_for_3_D_MR_image_segmentation_ DB - PRIME DP - Unbound Medicine ER -