title: An improved variational level set method for MR image segmentation and bias field correction.
abstract: In this paper, we propose an improved variational level set approach to correct the bias and to segment the magnetic resonance (MR) images with inhomogeneous intensity. First, we use a Gaussian distribution with bias field as a local region descriptor in two-phase level set formulation for segmentation and bias field correction of the images with inhomogeneous intensities. By using the information of the local variance in this descriptor, our method is able to obtain accurate segmentation results. Furthermore, we extend this method to three-phase level set formulation for brain MR image segmentation and bias field correction. By using this three-phase level set function to replace the four-phase level set function, we can reduce the number of convolution operations in each iteration and improve the efficiency. Compared with other approaches, this algorithm demonstrates a superior performance.
authors.affiliation: School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China.
authors.forename: Tianming
authors.initials: T
authors.lastname: Zhan
authors.forename: Jun
authors.initials: J
authors.lastname: Zhang
authors.forename: Liang
authors.initials: L
authors.lastname: Xiao
authors.forename: Yunjie
authors.initials: Y
authors.lastname: Chen
authors.forename: Zhihui
authors.initials: Z
authors.lastname: Wei
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity