A topology preserving level set method for geometric deformable models

Xiao Han, Chenyang Xu, Jerry L. Prince

Research output: Contribution to journalArticlepeer-review

392 Scopus citations


Active contour and surface models, also known as deformable models, are powerful image segmentation techniques. Geometric deformable models Implemented using level set methods have advantages over parametric models due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models - the ability to automatically handle topology changes - turns out to be a liability In applications where the object to be segmented has a known topology that must be preserved. In this paper, we present a new class of geometric deformable models designed using a novel topology-preserving level set method, which achieves topology preservation by applying the simple point concept from digital topology. These new models maintain the other advantages of standard geometric deformable models including subpixel accuracy and production of nonintersecting curves or surfaces. Moreover, since the topology-preserving constraint is enforced efficiently through local computations, the resulting algorithm incurs only nominal computational overhead over standard geometric deformable models. Several experiments on simulated and real data are provided to demonstrate the performance of this new deformable model algorithm.

Original languageEnglish (US)
Pages (from-to)755-768
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number6
StatePublished - Jun 2003


  • Active contours
  • Digital topology
  • Geometric deformable model
  • Level set method
  • Simple points
  • Topological constraint
  • Topology preservation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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