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GPU-accelerated level-set segmentation

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Abstract

The level-set method, a technique for the computation of evolving interfaces, is a solution commonly used to segment images and volumes in medical applications. GPUs have become a commodity hardware with hundreds of cores that can execute thousands of threads in parallel, and they are nowadays ideal platforms to execute computational intensive tasks, such as the 3D level-set-based segmentation, in real time. In this paper, we propose two GPU implementations of the level-set-based segmentation method called Fast Two-Cycle. Our proposals perform computations in independent domains called tiles and modify the structure of the original algorithm to better exploit the features of the GPU. The implementations were tested with real images of brain vessels and a synthetic MRI image of the brain. Results show that they execute faster than a CPU-sequential implementation of the same method, without any significant loss of the segmentation quality and without requiring distributed parallel computer infrastructures.

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Acknowledgments

This work was supported in part by the Ministry of Science and Innovation, Government of Spain, and FEDER funds under contract TIN 2010-17541, and by the Xunta de Galicia under contracts 08TIC001206PR and 2010/28. Julián Lamas-Rodríguez acknowledges financial support from the Ministry of Science and Innovation, Government of Spain, under a MICINN-FPI grant.

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Authors and Affiliations

  1. Centro de Investigación en Tecnoloxías da Información da Universidade de Santiago de Compostela (CITIUS), Santiago de Compostela, Spain

    Julián Lamas-Rodríguez & Dora B. Heras

  2. Department of Electronics and Computer Science, University of Santiago de Compostela, Santiago de Compostela, Spain

    Francisco Argüello & Montserrat Bóo

  3. Department of Visualization and Data Analysis, Konrad-Zuse-Zentrum für Informationstechnik, Berlin, Germany

    Dagmar Kainmueller & Stefan Zachow

Authors
  1. Julián Lamas-Rodríguez
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  2. Dora B. Heras
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  3. Francisco Argüello
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  4. Dagmar Kainmueller
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  5. Stefan Zachow
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  6. Montserrat Bóo
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Correspondence to Julián Lamas-Rodríguez.

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Lamas-Rodríguez, J., Heras, D.B., Argüello, F. et al. GPU-accelerated level-set segmentation. J Real-Time Image Proc 12, 15–29 (2016). https://doi.org/10.1007/s11554-013-0378-6

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