Robust Classification and Analysis of Anatomical Surfaces Using 3D Skeletons

Abstract

We present a method for computing a surface classifier that can be used to detect convex ridges on voxel surfaces extracted from 3D scans. In contrast to classical approaches based on (discrete) curvature computations, which can be sensitive to various types of noise, we propose here a new method that detects convex ridges on such surfaces, based on the computation of the surface’s 3D skeleton. We use a suitable robust, noise-resistant skeletonization algorithm to extract the full 3D skeleton of the given surface, and subsequently compute a surface classifier that separates convex ridges from quasi-flat regions, using the feature points of the simplified skeleton. We demonstrate our method on voxel surfaces extracted from actual anatomical scans, with a focus on cortical surfaces, and compare our results with curvature-based classifiers. As a second application of the 3D skeleton, we show how a partitioning of the brain skeleton can be used in a preprocessing step for the brain surface analysis