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Related Concept Videos

Super-resolution Fluorescence Microscopy01:37

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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Single Molecule Fluorescence Microscopy on Planar Supported Bilayers
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Parallel implementations to accelerate the autofocus process in microscopy applications.

Juan C Valdiviezo-N1, Francisco J Hernandez-Lopez2, Carina Toxqui-Quitl3

  • 1CONACYT-Centro de Investigación en Ciencias de Información Geoespacial, Yucatán, México.

Journal of Medical Imaging (Bellingham, Wash.)
|January 21, 2020
PubMed
Summary
This summary is machine-generated.

Dedicated hardware significantly accelerates microscopy autofocus algorithms. Implementing autofocus functions on multicore CPUs and GPUs reduced computation time by up to 23x, enabling real-time applications.

Keywords:
Hyper-Qautofocus functionsgraphic processing unitmicroscopy imagesmulticore central processing unitnested parallelismparallel computingtuberculosis dataset

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Area of Science:

  • Microscopy
  • Computational Imaging
  • Hardware Acceleration

Background:

  • Autofocus algorithms in microscopy rely on image sharpness analysis.
  • These autofocus functions (AFs) are computationally intensive due to multi-image processing.

Purpose of the Study:

  • To evaluate the performance of dedicated hardware for speeding up autofocus processes.
  • To compare the implementation of autofocus algorithms on multicore CPU and GPU architectures.

Main Methods:

  • Implemented four autofocus algorithms on multicore CPU and GPU platforms.
  • Conducted experiments using 300 image stacks containing tuberculosis bacilli.

Main Results:

  • Achieved acceleration of autofocus computation time by up to 23 times compared to serial processing.
  • Demonstrated significant speed-up for specific autofocus algorithms on parallel hardware.

Conclusions:

  • Optimal utilization of multicore CPUs and GPUs effectively accelerates autofocus.
  • Hardware acceleration is viable for real-time microscopy autofocus applications.