Deconvolution of 3d fluorescence microscopy images using graphics processing units

  • Authors:

  • Luisa D'Amore;Livia Marcellino;Valeria Mele;Diego Romano

  • Affiliations:

  • University of Naples Federico II, Naples, Italy;Centro Direzionale, University of Naples Parthenope, Naples, Italy;University of Naples Federico II, Naples, Italy;ICAR - CNR, Naples, Italy

  • Venue:
  • PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
  • Year:
  • 2011

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Abstract

We consider the deconvolution of 3D Fluorescence Microscopy RGB images, describing the benefits arising from facing medical imaging problems on modern graphics processing units (GPUs), that are non expensive parallel processing devices available on many up-to-date personal computers. We found that execution time of CUDA version is about 2 orders of magnitude less than the one of sequential algorithm. Anyway, the experiments lead some reflections upon the best setting for the CUDA-based algorithm. That is, we notice the need to model the GPUs architectures and their characteristics to better describe the performance of GPU-algorithms and what we can expect of them.