Automated Microorganisms Activity Detection on the Early Growth Stage Using Artificial Neural Networks
Proceedings of SPIE
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.
laser speckle, microorganism activity estimation, neural networks, non-contact estimation
Bļizņuks, D., Lihachev, A., Liepins, J., Uteshev, D., Čižovs, J., Bondarenko, A., Boločko, K. Automated Microorganisms Activity Detection on the Early Growth Stage Using Artificial Neural Networks. In: Proceedings of SPIE, Germany, Minhene, 26-27 June, 2019. -: SPIE, 2019, pp.1-6. ISSN 1605-7422. e-ISSN 2410-9045. Available from: doi:10.1117/12.2527193