Quantitative examination of a novel clustering method using magnetic resonance diffusion tensor tractography. Segmentation of corpus callosum, and selection of subdivisions. Tracts are grouped into clusters according to similarity of shape and location and are colour coded accordingly (A). Clusters are then selected (B), red highlighted from left to right, as genu, premotor and supplementary motor projections, motor projections, sensory projections, and finally parietal temporal and occipital projections. Read more here

Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Segmentation of a synthetic example. Reorienting diffusion information based on the representative streamline (top left) result in almost uniform tensor distributions interior and exterior to the fiber bundle. While segmentation for the original data is difficult and leads to unsatisfactory results, segmentation of the reoriented data is much easier leading to a faithful segmentation with the proposed approach. For both segmentations, k = 10, θ = 0.01 λ = 0.7. Read more here

An MRI study of age-related white and gray matter volume changes in the rhesus monkey. Automated segmentation procedure. Interleaved proton density weighted (PDW) and T2-weighted images (T2w) served as the input for the segmentation pipeline. An intracranial cavity mask (ICC) was created, separating “brain” from “non-brain" voxels. Brain voxels were segmented into gray matter, white matter and CSF classes using the 3D Expectation-Maximization (3D EM) algorithm. An a priori atlas (Atlas) containing 14 regions of interest (ROI’s) segmented and then elastically matched to the 3D EM results to produce an anatomically based segmentation (TDS). Read more here