title: A modified method for MRF segmentation and bias correction of MR image with intensity inhomogeneity.
abstract: Markov random field (MRF) model is an effective method for brain tissue classification, which has been applied in MR image segmentation for decades. However, it falls short of the expected classification in MR images with intensity inhomogeneity for the bias field is not considered in the formulation. In this paper, we propose an interleaved method joining a modified MRF classification and bias field estimation in an energy minimization framework, whose initial estimation is based on k-means algorithm in view of prior information on MRI. The proposed method has a salient advantage of overcoming the misclassifications from the non-interleaved MRF classification for the MR image with intensity inhomogeneity. In contrast to other baseline methods, experimental results also have demonstrated the effectiveness and advantages of our algorithm via its applications in the real and the synthetic MR images.
authors.affiliation: University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China.
authors.forename: Mei
authors.initials: M
authors.lastname: Xie
authors.forename: Jingjing
authors.initials: J
authors.lastname: Gao
authors.forename: Chongjin
authors.initials: C
authors.lastname: Zhu
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D066128:Gray Matter / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D012984:Software; D013223:Statistics as Topic; D066127:White Matter / Q000473:pathology