Automated microorganisms activity detection on the early growth stage using artificial neural networks

Dmitrijs Bliznuks; Alexey Lihachev; Janis Liepins; Dilshat Uteshev; Yuriy Chizhov; Andrey Bondarenko; Katrina Bolochko

doi:10.1117/12.2527193

22 July 2019 Automated microorganisms activity detection on the early growth stage using artificial neural networks

Dmitrijs Bliznuks, Alexey Lihachev, Janis Liepins, Dilshat Uteshev, Yuriy Chizhov, Andrey Bondarenko, Katrina Bolochko

Proceedings Volume 11075, Novel Biophotonics Techniques and Applications V; 110751Q (2019) https://doi.org/10.1117/12.2527193
Event: European Conferences on Biomedical Optics, 2019, Munich, Germany

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.

Citation Download Citation

Dmitrijs Bliznuks, Alexey Lihachev, Janis Liepins, Dilshat Uteshev, Yuriy Chizhov, Andrey Bondarenko, and Katrina Bolochko "Automated microorganisms activity detection on the early growth stage using artificial neural networks", Proc. SPIE 11075, Novel Biophotonics Techniques and Applications V, 110751Q (22 July 2019); https://doi.org/10.1117/12.2527193

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