Medial Axis Approximation with Discrete Centroidal Voronoi Diagrams On Discrete Data

Abstract

In this paper, we present a novel method for medial axis approximation based on Constrained Centroidal Voronoi Diagram of discrete data (image, volume). The proposed approach is based on the shape boundary subsampling by a clustering approach which generates a Voronoi Diagram well suited for Medial Axis extraction. The resulting Voronoi Diagram is further filtered so as to capture the correct topology of the medial axis. The resulting medial axis appears largely invariant with respect to typical noise conditions in the discrete data. The method is tested on various synthetic as well as real images. We also show an application of the approximate medial axis to the sizing field for triangular and tetrahedral meshing