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. An efficient distance-based procedure to generate the skeleton of large, complex 3D images such as CT, MRI data of human organ is then described. The proposed 3D Voxel Coding (3DVC) algorithm, is based on Discrete Euclidean Distance Transform. Instead of actual distance, each interior voxel (3D pixel) in the 3D image object is labeled with an integer code according to its relative distance from the object border for computation efficiency. All center voxels, which are the furthest away from the object border, are then collected and thinned to form clusters. To preserve the topology of the 3D image object, a cluster-labeling heuristic is then applied to order the clusters, and to recursively connect the next nearest clusters, gradually reducing the total number of disjoint clusters, to generate one final connected skeleton for each 3D object. 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, and perhaps to incorporate automatic machine intelligence decisions. Parallel version of 3DVC is also introduced to further enhance skeletonization speed.