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

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 ar...

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Bibliographic Details
Published in:The Visual computer 2011-02, Vol.27 (2), p.85-95
Main Authors: Schmid, Jérôme, Iglesias Guitián, José A., Gobbetti, Enrico, Magnenat-Thalmann, Nadia
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Graphics
Computer Science
Deformation
Formability
Graphics processing units
Image Processing and Computer Vision
Image segmentation
Medical imaging
Original Article
Parallel processing
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Summary: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.
ISSN:0178-2789
1432-2315
DOI:10.1007/s00371-010-0532-0