DocumentCode :
2353708
Title :
A topology preserving deformable model using level sets
Author :
Han, Xiao ; Xu, Chenyang ; Prince, Jerry L.
Author_Institution :
Center for Imaging Science, Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD, USA
Volume :
2
fYear :
2001
fDate :
2001
Abstract :
Active contour and surface models, also known as deformable models, constitute a class of powerful segmentation techniques. Geometric deformable models implemented via level-set methods have advantages over parametric ones 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 objects to be segmented have a known topology that must be preserved. In this paper, we present a geometric deformable model that preserves topology using the simple point concept from digital topology. This algorithm maintains the other advantages of standard geometric deformable models including sub-pixel accuracy and production of nonintersecting curves (or surfaces). Several experiments on simulated and real data are provided to demonstrate the performance of the proposed algorithm.
Keywords :
computational geometry; edge detection; image segmentation; topology; active contour models; deformable models; edge detection; image segmentation; segmentation techniques; shape modeling; surface models; topology; visual tracking; Active contours; Brain; Deformable models; Heart; Image edge detection; Image segmentation; Lagrangian functions; Level set; Production; Topology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on
ISSN :
1063-6919
Print_ISBN :
0-7695-1272-0
Type :
conf
DOI :
10.1109/CVPR.2001.991042
Filename :
991042
Link To Document :
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