Skeletnization and its applications - a review

Skeletonization provides a compact yet effective representation of an object, which is useful in many low- as well as high-level image-related tasks including object representation, retrieval, manipulation, matching, registration, tracking, recognition, compression, medical imaging applications. Also, it facilitates efficient characterization of topology, geometry, scale, and other related local properties in an object. Despite that the notion of skeletonization is well-defined in a continuous space, in the context of image processing and computer vision, it is often described using procedural approaches. Several computational approaches are available in literature toward extracting the skeleton of an object, some of which are widely different even at the level of of their basic principles. In this chapter, we present a comprehensive and concise survey of different skeletonization principles and algorithms, and discuss their properties, challenges, and benefits. Different important aspects of skeletonization, namely, topology preservation, parallelization, multi-scale skeletonization, skeleton simplification and punning approaches are discussed. Finally, various applications of skeletonization are reviewed and the fundamental issues related to the analysis of performance of different skeletonization algorithms are debated.