Efficient Medial Voxel Extraction for Large Volumetric Models

Abstract

Here we propose a method for medial voxel extraction from large volumetric models based on an out-of-core framework. The method improves upon geodesic-based approaches to enable the handling of large objects. First, distance fields are constructed from input volumes using an out-of-core algorithm. Second, medial voxels are extracted from these distance fields through multi-phase evaluation processes. Trivial medial or non-medial voxels are evaluated by the low-cost pseudo-geodesic distance method first, and the more expensive geodesic distance computation is run last. Using this strategy allows most of the voxels to be extracted in the low-cost process. This paper outlines a number of results regarding the extraction of medial voxels from large volumetric models. Our method also works in parallel, and we demonstrate that computation time becomes even shorter in multi-core environments.