title: MR image segmentation and bias field estimation based on coherent local intensity clustering with total variation regularization.
abstract: Though numerous segmentation algorithms have been proposed to segment brain tissue from magnetic resonance (MR) images, few of them consider combining the tissue segmentation and bias field correction into a unified framework while simultaneously removing the noise. In this paper, we present a new unified MR image segmentation algorithm whereby tissue segmentation, bias correction and noise reduction are integrated within the same energy model. Our method is presented by a total variation term introduced to the coherent local intensity clustering criterion function. To solve the nonconvex problem with respect to membership functions, we add auxiliary variables in the energy function such as Chambolle's fast dual projection method can be used and the optimal segmentation and bias field estimation can be achieved simultaneously throughout the reciprocal iteration. Experimental results show that the proposed method has a salient advantage over the other three baseline methods on either tissue segmentation or bias correction, and the noise is significantly reduced via its applications on highly noise-corrupted images. Moreover, benefiting from the fast convergence of the proposed solution, our method is less time-consuming and robust to parameter setting.
authors.affiliation: School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China.
authors.forename: Xiaoguang
authors.initials: X
authors.lastname: Tu
authors.affiliation: School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China. jingjing.gao@uestc.edu.cn.
authors.forename: Jingjing
authors.initials: J
authors.lastname: Gao
authors.affiliation: School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China.
authors.forename: Chongjing
authors.initials: C
authors.lastname: Zhu
authors.affiliation: School of Biomedical Engineering, Shenzhen University, Shenzhen, China.
authors.forename: Jie-Zhi
authors.initials: JZ
authors.lastname: Cheng
authors.affiliation: School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Ma
authors.affiliation: School of Automation, Chongqing University, Chongqing, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Dai
authors.affiliation: School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China.
authors.forename: Mei
authors.initials: M
authors.lastname: Xie
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D016000:Cluster Analysis; D066128:Gray Matter / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D066127:White Matter / Q000473:pathology
keywords: Bias correction; Chambolle’s fast dual projection; Nonconvex term; Tissue classification