Geometric deformable models based on the level set method have become very popular in the last several years. 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 our previously presented topology-preserving geometric deformable model (TGDM). In this paper, we incorporate an alternative adaptive grid technique called the moving grid method into the geometric deformable model framework. We find that it is simpler to implement than grid refinement, requiring no large, complex, hierarchical data structures. It also limits the number of contour vertices in the final contour and supports the incorporation of the topology-preserving constraint of TGDM. After presenting the algorithm, we demonstrate its performance using both simulated and real images.
|Original language||English (US)|
|Journal||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|State||Published - 2003|
|Event||2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Madison, WI, United States|
Duration: Jun 18 2003 → Jun 20 2003
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition