AUTOMATIC SUBCORTICAL TISSUE SEGMENTATION OF MR IMAGES USING OPTIMUM-PATH FOREST CLUSTERING

Abstract

Automatic MR-image segmentation of brain tissues is an important issue in neuroimaging. For instance, it is a key methodological component of a popular technique denominated voxel-based morphometry (VBM), which quantifies gray-matter (GM) volumes from MR images. However, segmentation accuracy in some subcortical regions on the basis of extant methods is not satisfactory, compromising VBM results. We combine a probabilistic atlas and a fast clustering approach based on optimum connectivity between voxels in their feature space. The algorithm exploits local image properties and global information from the atlas as features to group GM and white-matter (WM) voxels in distinct clusters, and uses the total probability values inside the clusters to label them as GM or WM. This new method is validated in the region of the thalamus and outperformed two widely used methods packaged in SPM and FSL.