An integrated geometrical and stochastic approach for accurate infant brain extraction

Abstract

This paper presents a novel approach for extracting the brain from 3D T1-weighted MR images. The proposed approach combines a stochastic two-level Markov-Gibbs random field (MGRF) image model with a geometric model that parcels the brain into a set of nested iso-surfaces using a fast marching level setmethod. The classification of each brain voxel found on the iso-surfaces is performed based on the first-order (a linear combination of discrete gaussian (LCDG) model) and second-order (an MGRF model with analytically estimated parameters) visual appearance features of the brain structures. Our approach is tested on 280 infant 3D MR brain scans and evaluated on 9 data sets using the Dice coefficient, the 95-percentile modified Hausdorff distance, and absolute brain volume difference. Experimental results showed that the fusion of the stochastic and geometric models of brain MRI data has led to more accurate brain extraction, when compared with other widely-used brain extraction tools, such as BET, BET2, and brain surface extractor (BSE).