Skeletons in Digital Image Processing

Abstract

Skeletonization is a transformation of a component of a digital image into a subset of the original component. There are different categories of skeletonization methods: one category is based on distance transforms, and a specified subset of the transformed image is a distance skeleton. The original component can be reconstructed from the distance skeleton. Another category is defined by thinning approaches; and the result of skeletonization using thinning algorithms should be a connected set of digital curves or arcs. Motivations for interest in skeletonization algorithms are the need to compute a reduced amount of data or to simplify the shape of an object in order to find features for recognition algorithms and classifications. Additionally the transformation of a component into an image showing essential characteristics can eliminate local noise at the frontier. Thinning algorithms are a very active area of research, with a main focus on connectivity preserving methods allowing parallel implementation. There are hundreds of publications on different aspects of these transformations. This report reviews contributions in this area with respect to properties of algorithms and characterizations of simple points, and informs about a few new results.