Heterogeneous Platform Programming for High Performance Medical Imaging Processing
Medical imaging processing algorithms can be computationally very demanding. Currently, computers with multiple computing devices, such as multi-core CPUs, GPUs, and FPGAs, have emerged as powerful processing environments. These so called heterogeneous platforms have potential to significantly accelerate medical imaging applications. In this study, we evaluate the potential of heterogeneous platforms to improve the processing speed of medical imaging applications by using a new framework named FlowCL. This framework facilitates the development of parallel applications for heterogeneous platforms. We compared an implementation of region growing based method to automated cerebral infarct volume measurement with a new implementation targeted for heterogeneous platforms. The results of this new implementation agree well with the original implementation and they are obtained with significant speed-up comparing to the sequential implementation.
Keywordsdataflow framework heterogeneous computing heterogeneous platforms medical imaging processing OpenCL parallel programming
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- 2.Barak, A., Ben-Nun, T., Levy, E., Shiloh, A.: A package for opencl based heterogeneous computing on clusters with many gpu devices. In: 2010 IEEE International Conference on Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS), Heraklion, pp. 1–7 (2010)Google Scholar
- 3.Boers, A., Marquering, H., Jochem, J., Besselink, N., Berkhemer, O., van der Lugt, A., Beenen, L., Majoi, C.: Automated cerebral infarct volume measurement in follow-up noncontrast ct scans of patients with acute ischemic stroke. American Journal of Neuroradiology (2013)Google Scholar
- 4.van Geldermalsen, S.: Work In Progress Thesis - FlowCL. Master’s thesis, University of Amsterdam (2013)Google Scholar
- 5.Khronos OpenCL Working Group: The opencl specification (2012)Google Scholar
- 7.MATLAB: version 22.214.171.1243 (R2012b). The MathWorks, Inc., Natick, Massachusetts (2012)Google Scholar
- 10.Spafford, K., Meredith, J., Vetter, J.: Maestro: Data orchestration and tuning for opencl devices (2010)Google Scholar