Deconvolution of 3D Fluorescence Microscopy Images Using Graphics Processing Units

  • Luisa D’Amore
  • Livia Marcellino
  • Valeria Mele
  • Diego Romano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7203)

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.

Keywords

Execution Time Graphic Processing Unit Discrete Fourier Transform Fast Fourier Transform Algorithm Inverse Discrete Fourier Transform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Luisa D’Amore
    • 1
  • Livia Marcellino
    • 2
  • Valeria Mele
    • 1
  • Diego Romano
    • 3
  1. 1.University of Naples Federico IINaplesItaly
  2. 2.Centro DirezionaleUniversity of Naples ParthenopeNaplesItaly
  3. 3.ICAR - CNRNaplesItaly

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