Image processing for automated analysis of the fluorescence in-situ hybridization (FISH) microscopic images

  • Authors:

  • Jan Schier;Bohumil Kovář;Eduard Kočárek;Michal Kuneš

  • Affiliations:

  • Institute of Information Theory and Automation of the ASCR, Prague 8, Czech Republic;Institute of Information Theory and Automation of the ASCR, Prague 8, Czech Republic;Department of Biology and Medical Genetics, 2nd Faculty of Medicine, Charles University Prague, Prague 5, Czech Republic;Institute of Information Theory and Automation of the ASCR, Prague 8, Czech Republic

  • Venue:
  • ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
  • Year:
  • 2011

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Abstract

The paper describes automated segmentation and analysis of the microscopic images resulting from fluorescence in-situ hybridization (FISH) analysis. FISH is a popular molecular cytogenetic method. The output of a single FISH analysis is a set of several tens or hundreds microscopic images -- a single evaluated sample is of roughly 20mm diameter. The goal of an automated evaluation is to replace the subjective evaluation of images by the laboratory technician to achieve higher uniformity of results. Following explanation of the principle of the method and the typical contents of images, the processing flow of image segmentation is outlined and the results are presented on several example images. With emphasis on a low-cost solution, the ITK library is used for implementation.