A novel MRA framework based on integrated global and local analysis for accurate segmentation of the cerebral vascular system
A novel, fully automated segmentation framework that can extract cerebral blood vessels larger than 1 mm diameter from time-offlight magnetic resonance angiography (TOF-MRA) scans was developed. The segmentation framework consists of the following main steps: 1) Following bias correction, the TOF-MRA image is modeled as a a three-dimensional (3-D) generalized Gauss-Markov random field (GGMRF) where pairwise interactions between the 26-neighborhood voxels enhance the homogeneity of brain and vascular tissues while preserving the 3-D edges between them. 2) To automatically extract the region of interest, a novel approach was developed to remove the skull from TOF-MRA images based on the integration of the Markov-Gibbs random field (MGRF) model of TOF-MRA visual appearance with a geometric deformable model (brain isosurface) that preserves brain topology during the extraction process. 3) The initial vascular system is extracted using Bayes classifier following the estimation of the marginal probability density of TOF-MRA voxel values for both blood vessels and other brain tissues, modeled as a linear combination of discrete Gaussians (LCDG). 4) To enhance the extraction of small vessels, a seed-generation refinement algorithm was used to identify points within regions likely to contain small vessels overlooked by the previous step. 5) Finally, a 3-D region-growing connected components algorithm was applied to extract the final, connected vascular tree. The proposed approach was tested on in-vivo data (200 TOF-MRA data sets), and qualitatively and quantitatively validated by an MRA expert (ground truth). The proposed approach resulted in high segmentation accuracy with 86% Dice similarity coefficient, and 10% absolute volume difference.