International Journal of Computer Vision

, Volume 84, Issue 1, pp 63–79 | Cite as

A Moving Grid Framework for Geometric Deformable Models

  • Xiao Han
  • Chenyang Xu
  • Jerry L. PrinceEmail author


Geometric deformable models based on the level set method have become very popular in the last decade. To overcome an inherent limitation in accuracy while maintaining computational efficiency, adaptive grid techniques using local grid refinement have been developed for use with these models. This strategy, however, requires a very complex data structure, yields large numbers of contour points, and is inconsistent with the implementation of topology-preserving geometric deformable models (TGDMs). In this paper, we investigate the use of an alternative adaptive grid technique called the moving grid method with geometric deformable models. In addition to the development of a consistent moving grid geometric deformable model framework, our main contributions include the introduction of a new grid nondegeneracy constraint, the design of a new grid adaptation criterion, and the development of novel numerical methods and an efficient implementation scheme. The overall method is simpler to implement than using grid refinement, requiring no large, complex, hierarchical data structures. It also offers an extra benefit of automatically reducing the number of contour vertices in the final results. After presenting the algorithm, we demonstrate its performance using both simulated and real images.


Adaptive grid method Geometric deformable model Deformation moving grid Topology preservation Level set method 


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.CMS Software, Elekta Inc.St. LouisUSA
  2. 2.Siemens Corporate ResearchPrincetonUSA
  3. 3.Center for Imaging Science, Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreUSA

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