title: A Modified Brain MR Image Segmentation and Bias Field Estimation Model Based on Local and Global Information.
abstract: Because of the poor radio frequency coil uniformity and gradient-driven eddy currents, there is much noise and intensity inhomogeneity (bias) in brain magnetic resonance (MR) image, and it severely affects the segmentation accuracy. Better segmentation results are difficult to achieve by traditional methods; therefore, in this paper, a modified brain MR image segmentation and bias field estimation model based on local and global information is proposed. We first construct local constraints including image neighborhood information in Gaussian kernel mapping space, and then the complete regularization is established by introducing nonlocal spatial information of MR image. The weighting between local and global information is automatically adjusted according to image local information. At the same time, bias field information is coupled with the model, and it makes the model reduce noise interference but also can effectively estimate the bias field information. Experimental results demonstrate that the proposed algorithm has strong robustness to noise and bias field is well corrected.
authors.affiliation: College of Automation, Harbin Engineering University, Harbin 150001, China.
authors.forename: Wang
authors.initials: W
authors.lastname: Cong
authors.affiliation: College of Automation, Harbin Engineering University, Harbin 150001, China; Electronic Engineering College, Heilongjiang University, Harbin 150080, China.
authors.forename: Jianhua
authors.identifier: 0000-0002-9548-7274
authors.initials: J
authors.lastname: Song
authors.affiliation: College of Automation, Harbin Engineering University, Harbin 150001, China.
authors.forename: Kuan
authors.initials: K
authors.lastname: Luan
authors.affiliation: College of Automation, Harbin Engineering University, Harbin 150001, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Liang
authors.affiliation: College of Automation, Harbin Engineering University, Harbin 150001, China.
authors.forename: Lei
authors.identifier: 0000-0002-6982-7124
authors.initials: L
authors.lastname: Wang
authors.affiliation: College of Automation, Harbin Engineering University, Harbin 150001, China.
authors.forename: Xingcheng
authors.initials: X
authors.lastname: Ma
authors.affiliation: College of Automation, Harbin Engineering University, Harbin 150001, China.
authors.forename: Jin
authors.identifier: 0000-0002-6033-0381
authors.initials: J
authors.lastname: Li