On the generation and pruning of skeletons using generalized Voronoi diagrams

  • Authors:

  • Hongzhi Liu;Zhonghai Wu;D. Frank Hsu;Bradley S. Peterson;Dongrong Xu

  • Affiliations:

  • School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, PR China and MRI Unit, Department of Psychiatry, Columbia University & New York State Psychiatric Institu ...;School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, PR China and School of Software and Microelectronics, Peking University, Beijing 102600, PR China;Department of Computer and Information Science, Fordham University, New York, NY 10023, USA;MRI Unit, Department of Psychiatry, Columbia University & New York State Psychiatric Institute, New York, NY 10032, USA;MRI Unit, Department of Psychiatry, Columbia University & New York State Psychiatric Institute, New York, NY 10032, USA

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2012

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Abstract

Skeletonization is a necessary process in a variety of applications in image processing and object recognition. However, the concept of a skeleton, defined using either the union of centers of maximal discs or the union of points with more than one generating points, was originally formulated in continuous space. When they are applied to situation in discrete space, the resulting skeletons may become disconnected and further works are needed to link them. In this paper, we propose a novel skeletonization method which extends the concept of a skeleton to include both continuous and discrete space using generalized Voronoi diagrams. We also present a skeleton pruning method which is able to remove noisy branches by evaluating their significance. Three experimental results demonstrate that: (1) our method is stable across a wide range of shapes, and (2) it performs better in accuracy and robustness than previous approaches for processing shapes whose boundaries contain substantial noise.