The laser speckle subpixel correlation method was proposed to investigate bacterial activity moving from center to the colony edges. Results could facilitate epidemiological analysis and improve mathematical models of colony growth.
This study presents autonomous system for microorganisms’ growth analysis in laboratory environment. As shown in previous research, laser speckle analysis allows detecting submicron changes of substrate with growing bacteria. By using neural networks for speckle analysis, it is possible to develop autonomous system, that can evaluate microorganisms’ growth by using cheap optics and electronics elements.
System includes embedded processing module, CMOS camera, 670nm laser diode and optionally WiFi module for connecting to external image storage system. Due to small size, system could be fully placed in laboratory incubator with constant humidity and temperature. By using laser diode, Petri dish with microorganisms’ substrate is illuminated with speckle pattern. Embedded camera and processing system obtain images and stores them for processing with neural network.
Neural network utilizes “3D ConvNets” architecture with ability to encode not only spatial speckle variance, but also their changes in time. Convolutive approach allows significantly reduce the number of trained parameters, therefore reducing training and detection time. Neural network training used 200 bacteria colonies and additional 300 areas without bacteria. In the result, trained neural network reaches 0.95 accuracy score, that proves correctness of the approach.
The study aims at development and laboratory approbation of non-contact optical technique for early evaluation of microbial activity. Microorganisms’ activity is estimated by laser speckle contrast imaging technique in combination with image processing of obtained time varying speckle patterns. Laser speckle patterns were captured by CMOS sensor during illumination of growing bacteria colonies by low power (<30 mW, 635 nm) stabilized coherent light source. To validate proposed technique and image processing algorithm the vibrio natriegens bacteria are used. After analysis of several different experiments the following results were obtained: In the central part of the colony activity can be seen in 2.5-3 hours. Thus, earlier detection of bacterial activity is expected, i.e., earlier than 2-3 hours, which is much earlier than the standard counting methods used to count colony forming units (CFU).
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