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A GPU framework for parallel segmentation of volumetric images using discrete deformable models

Jérôme Schmid, José Antonio Iglesias Guitián, Enrico Gobbetti, and Nadia Magnenat-Thalmann

2010

Abstract

Although research in image segmentation has been very active during the last decades, it is still a very challenging problem. 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 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.

Reference and download information

Jérôme Schmid, José Antonio Iglesias Guitián, Enrico Gobbetti, and Nadia Magnenat-Thalmann. A GPU framework for parallel segmentation of volumetric images using discrete deformable models. In Proc. 3DAnatomicalHuman Summer School, 2010.

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Bibtex citation record

@InProceedings{Schmid:2010:GFP,
    author = {J{\'e}r{\^o}me Schmid and Jos{\'e} Antonio {Iglesias Guiti{\'a}n} and Enrico Gobbetti and Nadia Magnenat-Thalmann},
    title = {A GPU framework for parallel segmentation of volumetric images using discrete deformable models},
    booktitle = {Proc. 3DAnatomicalHuman Summer School},
    year = {2010},
    abstract = { Although research in image segmentation has been very active during the last decades, it is still a very challenging problem. 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 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. },
    url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Schmid:2010:GFP'},
}