A GPU framework for parallel segmentation of volumetric images using discrete deformable models |
| |
Authors: | J��r?me Schmid Jos�� A Iglesias?Guiti��n Enrico Gobbetti Nadia Magnenat-Thalmann |
| |
Affiliation: | 1. MIRALab, University of Geneva, Battelle, 1227, Carouge, Switzerland 2. CRS4 Visual Computing Group, Polaris ed. 1, 09010, Pula, Italy 3. Institute for Media Innovation, Nanyang Technological University, Singapore, Singapore
|
| |
Abstract: | Despite the ability of current GPU processors to treat heavy parallel computation tasks, its use for solving medical image
segmentation problems is still not fully exploited and remains challenging. A lot of difficulties may arise related to, for
example, the different image modalities, noise and artifacts of source images, or the shape and appearance variability of
the structures to segment. Motivated by practical problems of image segmentation in the medical field, we present in this
paper a GPU framework based on explicit discrete deformable models, implemented over the NVidia CUDA architecture, aimed for
the segmentation of volumetric images. The framework supports the segmentation in parallel of different volumetric structures
as well as interaction during the segmentation process and real-time visualization of the intermediate results. Promising
results in terms of accuracy and speed on a real segmentation experiment have demonstrated the usability of the system. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|