title: An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation.
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.affiliation: Department of Computer Engineering and Information Technology, City University of Hong Kong, Hong Kong. itwcliew@cityu.edu.hk
authors.forename: Alan Wee-Chung
authors.initials: AW
authors.lastname: Liew
authors.forename: Hong
authors.initials: H
authors.lastname: Yan
mesh_terms: D000465:Algorithms; D019412:Anatomy, Cross-Sectional / Q000379:methods; D001921:Brain / Q000033:anatomy & histology*; D016000:Cluster Analysis; D003198:Computer Simulation; D005246:Feedback; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity