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 sur- faces 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.