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Automated microorganisms activity detection on the early growth stage using artificial neural networks
Author(s): Dmitrijs Bliznuks; Alexey Lihachev; Janis Liepins; Dilshat Uteshev; Yuriy Chizhov; Andrey Bondarenko; Katrina Bolochko
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Paper Abstract

The paper proposes an approach of a novel non-contact optical technique for early evaluation of microbial activity. Noncontact evaluation will exploit laser speckle contrast imaging technique in combination with artificial neural network (ANN) based image processing. Microbial activity evaluation process will comprise acquisition of time variable laser speckle patterns in given sample, ANN based image processing and visualization of obtained results. The proposed technology will measure microbial activity (like growth speed) and implement these results for counting live microbes. It is expected, that proposed technology will help to evaluate number of colony forming units (CFU) and return results two to six times earlier in comparison with standard counting methods used for CFU enumeration.

Paper Details

Date Published: 22 July 2019
PDF: 6 pages
Proc. SPIE 11075, Novel Biophotonics Techniques and Applications V, 110751Q (22 July 2019); doi: 10.1117/12.2527193
Show Author Affiliations
Dmitrijs Bliznuks, Riga Technical Univ. (Latvia)
Alexey Lihachev, Univ. of Latvia (Latvia)
Janis Liepins, Univ. of Latvia (Latvia)
Dilshat Uteshev, C.T.Co., Ltd. (Latvia)
Yuriy Chizhov, Riga Technical Univ. (Latvia)
Andrey Bondarenko, C.T.Co., Ltd. (Latvia)
Katrina Bolochko, Riga Technical Univ. (Latvia)


Published in SPIE Proceedings Vol. 11075:
Novel Biophotonics Techniques and Applications V
Arjen Amelink; Seemantini K. Nadkarni, Editor(s)

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