title: [A new unsupervised algorithm for image segmentation based on an inhomogeneous Markov random field model].
abstract: A new unsupervised algorithm for image segmentation is proposed using an inhomogeneous Markov random field (MRF) model, in which the parameter is estimated in fuzzy spel affinities. The proposed algorithm improved the accuracy of segmentation. Simulated brain MR image with different noise levels and clinical brain MR image were presented in the experiments. The results showed that the proposed algorithm was more powerful than conventional homogeneous MRF model-based ones and than the fuzzy c-means clustering algorithm as well.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China. libin371@fimmu.com
authors.forename: Bin
authors.initials: B
authors.lastname: Li
authors.forename: Wu-fan
authors.initials: WF
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008390:Markov Chains