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International Journal of Biomedical Imaging
Volume 2011 (2011), Article ID 137604, 15 pages
Research Article

On the Usage of GPUs for Efficient Motion Estimation in Medical Image Sequences

1Oxford e-Research Centre, University of Oxford, Oxford OX1 3QG, UK
2Institute for the Future of Computing, Oxford Martin School, University of Oxford, Oxford OX1 3BD, UK
3School of Computer Science, The University of Manchester, Manchester M13 9PL, UK
4Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK

Received 1 April 2011; Accepted 3 June 2011

Academic Editor: Yasser M. Kadah

Copyright © 2011 Jeyarajan Thiyagalingam et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Images are ubiquitous in biomedical applications from basic research to clinical practice. With the rapid increase in resolution, dimensionality of the images and the need for real-time performance in many applications, computational requirements demand proper exploitation of multicore architectures. Towards this, GPU-specific implementations of image analysis algorithms are particularly promising. In this paper, we investigate the mapping of an enhanced motion estimation algorithm to novel GPU-specific architectures, the resulting challenges and benefits therein. Using a database of three-dimensional image sequences, we show that the mapping leads to substantial performance gains, up to a factor of 60, and can provide near-real-time experience. We also show how architectural peculiarities of these devices can be best exploited in the benefit of algorithms, most specifically for addressing the challenges related to their access patterns and different memory configurations. Finally, we evaluate the performance of the algorithm on three different GPU architectures and perform a comprehensive analysis of the results.