A GPU framework for parallel segmentation of volumetric images using discrete deformable models
Jerome Schmid,
José A. Iglesias Guitián,
Enrico Gobbetti,
Nadia Magnenat-Thalmann
The Visual Computer - 2011
Download the publication :
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.
Images and movies
BibTex references
@Article{SIGM11,
author = {Schmid, J. and Iglesias Guitián, J. and Gobbetti, E. and Magnenat-Thalmann, N.},
title = {A GPU framework for parallel segmentation of volumetric images using discrete deformable models},
journal = {The Visual Computer},
year = {2011},
keywords = {volume segmentation, deformable models},
url = {https://publications.crs4.it/pubdocs/2011/SIGM11},
}
Other publications in the database