Variational level set combined with Markov random field modeling for simultaneous intensity non-uniformity correction and segmentation of MR images.
Noise and intensity non-uniformity are causing major difficulties in magnetic resonance (MR) image segmentation. This paper introduces a variational level set approach for simultaneous MR image segmentation and intensity non-uniformity correction. The proposed energy functional is based on local Gaussian intensity fitting with local means and variances. Furthermore, the proposed model utilizes Markov random fields to model the spatial correlation between neighboring pixels/voxels. The improvements achieved with our method are demonstrated by brain segmentation experiments with simulated and real magnetic resonance images with different noise and bias level. In particular, it is superior in term of accuracy as compared to LGDF and FSL-FAST methods.
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran.
Zahra
Z
Shahvaran
Kamran
K
Kazemi
Mohammad Sadegh
MS
Helfroush
Nassim
N
Jafarian
Negar
N
Noorizadeh
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008390:Markov Chains; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D016011:Normal Distribution