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Publikācija: Automated Microorganisms Activity Detection on the Early Growth Stage Using Artificial Neural Networks

Publication Type Scientific article indexed in SCOPUS or WOS database
Funding for basic activity Research project
Defending: ,
Publication language English (en)
Title in original language Automated Microorganisms Activity Detection on the Early Growth Stage Using Artificial Neural Networks
Field of research 2. Engineering and technology
Sub-field of research 2.2 Electrical engineering, Electronic engineering, Information and communication engineering
Research platform Information and Communication
Authors Dmitrijs Bļizņuks
Aleksey Lihachev
Janis Liepins
Dilshat Uteshev
Jurijs Čižovs
Andrey Bondarenko
Katrina Boločko
Keywords laser speckle, microorganism activity estimation, neural networks, non-contact estimation
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.
DOI: 10.1117/12.2527193
Hyperlink: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11075/2527193/Automated-microorganisms-activity-detection-on-the-early-growth-stage-using/10.1117/12.2527193.short 
Reference 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. Proceedings of SPIE, 2019, Vol. 11075, pp.1-6. ISSN 1605-7422. e-ISSN 2410-9045. Available from: doi:10.1117/12.2527193
Additional information Citation count:
  • Scopus  0
ID 30486