Abstract
Level set method based segmentation provides an efficient tool for topological and geometrical shape handling. Conventional level set surfaces are only $C^0$ continuous since the level set evolution involves linear interpolation to compute derivatives. Bajaj et al. present a higher order method to evaluate level set surfaces that are $C^2$ continuous, but are slow due to high computational burden. In this paper, we provide a higher order GPU based solver for fast and efficient segmentation of large volumetric images. We also extend the higher order method to multi-domain segmentation. Our streaming solver is efficient in memory usage.
Original language | English |
---|---|
Title of host publication | Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition |
Publisher | IEEE |
Publication date | 2010 |
Pages | 1063-6919 |
ISBN (Print) | 978-1-4244-6984-0 |
DOIs | |
Publication status | Published - 2010 |
Event | 2010 IEEE Conference on Computer Vision and Pattern Recognition - San Francisco, United States Duration: 13 Jun 2010 → 18 Jun 2010 Conference number: 23 https://ieeexplore.ieee.org/xpl/conhome/5521876/proceeding |
Conference
Conference | 2010 IEEE Conference on Computer Vision and Pattern Recognition |
---|---|
Number | 23 |
Country/Territory | United States |
City | San Francisco |
Period | 13/06/2010 → 18/06/2010 |
Internet address |
Keywords
- Volume segmentation
- level set method
- CUDA
- GPU computing
- Higher order segmentation