Segmentation of infant brain MR images based on adaptive shape prior and higher-order MGRF
This paper introduces a new framework for the segmentation of different brain structures from 3D infant MR brain images. The proposed segmentation framework is based on a shape prior built using a subset of co-aligned training images that is adapted during the segmentation process based on higher-order visual appearance characteristics of infant MRIs. These characteristics are described using voxel-wise image intensities and their spatial interaction features. In order to more accurately model the empirical grey level distribution of infant brain signals, a Linear Combination of Discrete Gaussians (LCDG) is used that has positive and negative components. Also to accurately account for the large inhomogeneity in infant MRIs, a higher-order Markov Gibbs Random Field (MGRF) spatial interaction model that integrates third- and fourth-order families with a traditional second-order model is proposed. The proposed approach was tested on 40 in-vivo infant 3D MR brain scans, having their ground truth created by an expert radiologist, using three metrics: the Dice coefficient, the 95-percentile modified Hausdorff distance, and the absolute brain volume difference. Experimental results promise an accurate segmentation of infant MR brain images compared to current open source segmentation tools.