Title :
A 2D moving grid geometric deformable model
Author :
Han, Xiao ; Xu, Chenyang ; Prince, Jerry L.
Author_Institution :
Johns Hopkins Univ., Baltimore, MD, USA
Abstract :
Geometric deformable models based on the level set method have become very popular. 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. However, this strategy 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.
Keywords :
computational geometry; image segmentation; partial differential equations; 2D moving grid; adaptive grid; contour point; contour vertex; hierarchical data structure; local grid refinement; partial differential equation; real image; simulated image; topology-preserving constraint; topology-preserving geometric deformable model; very complex data structure; Active contours; Active shape model; Computational efficiency; Data structures; Deformable models; Image analysis; Image segmentation; Lagrangian functions; Level set; Topology;
Conference_Titel :
Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on
Print_ISBN :
0-7695-1900-8
DOI :
10.1109/CVPR.2003.1211349