Segmentation of brain MR image using fuzzy local Gaussian mixture model with bias field correction
Pavan G S Pavan G S · IOSR Journal of VLSI and Signal processing · 2013
Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis.However, due to the existence of noise and intensity in-homogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy.Here, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation with bias field correction.This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM.Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.