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Vision based evaluation of the contamination level in high resolution images for industrial and clinical quality control applications
Author(s): Aurélien Launay; Guillaume Perrin; Ernest Hirsch
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Paper Abstract

In clinical environments and pharmaceutical industrial productions, early detection of contamination by microorganisms is a key point in terms of quality control. Determination of such contaminations relies on cultures in Petri dishes, the observation of which, through the detection of colonies of microorganisms, leads to methods enabling their determinations. However, these methods show limits in terms of speed and are rather tedious. To overcome these shortcomings, a method based on image analysis and deep learning is proposed to improve both detection of microorganism colonies in Petri dishes and quality of the quantitative determination of the contamination levels.

Paper Details

Date Published: 16 July 2019
PDF: 6 pages
Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 1117204 (16 July 2019); doi: 10.1117/12.2521442
Show Author Affiliations
Aurélien Launay, bioMérieux (France)
ICube (France)
Guillaume Perrin, bioMérieux (France)
Ernest Hirsch, ICube (France)


Published in SPIE Proceedings Vol. 11172:
Fourteenth International Conference on Quality Control by Artificial Vision
Christophe Cudel; Stéphane Bazeille; Nicolas Verrier, Editor(s)

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