Knowledge-driven segmentation of the central sulcus from human brain MR images

This paper presents a knowledge-driven algorithm to identify and segment the central sulcus (CS) from human brain MR images. The dataset is reformatted along the anterior and posterior commissures (AC-PC) plane first. Then, the 3D region within the two coronal planes passing through the AC and PC is defined as the region of interest (ROl) to search for all the sulci within it. The CS is the sulcus with the largest volume within the ROI. Together with the sulci, grey matter (GM) is included for the region growing in order to deal with the partial volume effect. The GM is removed through skeletonization. Experimental results are given.