6533b855fe1ef96bd12b1aac

RESEARCH PRODUCT

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

Yuriy ChizhovDmitrijs BliznuksAndrey BondarenkoDilshat UteshevAlexey LihachevKatrina BolochkoJanis Liepins

subject

Artificial neural networkComputer sciencebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONProcess (computing)Pattern recognitionImage processingVisualizationSpeckle patternEnumerationSpeckle imagingStage (hydrology)Artificial intelligencebusiness

description

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.

https://doi.org/10.1117/12.2527193