A 3 dimensional Digital Image Skeltonization using 3x3x3 Structuring Element

Abstract

Image Skeletonization promises to be a powerful complexity-cutting tool for compact shape description, pattern recognition, robot vision, animation, petrography pore space fluid flow analysis, model/analysis of bone/lung/circulation, and image compression for telemedicine. The existing image thinning/skeletonization techniques using boundary erosion, distance coding, and Voronoi diagram are first overviewed to assess/compare their feasibility of extending from 2D to 3D. Previously, skeletons have been a common tool for identifying shape components in a solid object. However, obtaining skeletons of a grayscale volume poses new challenges due to the lack of a clear boundary between object and background. In this paper we propose a fast, efficient and robust algorithm to generate the skeleton of large, complex 3D images such as CT, MRI data which make use of 3X3X3 structuring elements for processing. This algorithm has been developed in the frame work of cellular logic array processing. Cellular logic array processing is a logico mathematical paradigm developed using the fundamental notions of normal algorithms and cellular automata. The algorithm provides a straightforward computation which is robust and not sensitive to noise or object boundary complexity. Because 3D skeleton may not be unique, several application-dependent skeletonization options will be explored for meeting specific quality/speed requirements.