Laser speckle imaging techniques have become widespread in many areas where non-invasive remote measurements are needed. For example, medical and microbiological fields. This technique is applicable for monitoring the behavioral activity of microorganisms. In the current study, using experiments with fungi and bacteria, we compare signal and image processing algorithms for analyzing microorganism’s activity by laser speckle imaging techniques and demonstrate the advantages of the proposed method: sensitive sub-pixel correlation algorithm. The obtained results could allow to propose a technology for faster detection of bacterial and fungal growth in the culture medium. They could also be used to speed up the determination of antibacterial and antifungal susceptibility results.
Our previous studies have shown that laser speckle imaging with sensitive subpixel correlation analysis is able to detect bacterial growth activity and the pattern of colony growth. In the current study, we demonstrate the potential of this method to analyze fungal growth. We compare the characteristics of the signals obtained from bacteria and fungi. The obtained results will help to improve the parameters of the speckle image acquisition system and the signal processing algorithms useful for microorganism (both eukaryotic and prokaryotic) growth analyses and speeding up and facilitating microbiological diagnostics.
The laser speckle imaging technique with sub-pixel correlation analysis allows to identify changes in the sterile zone radius, and makes it possible to predict these changes significantly earlier than the disk diffusion method which is recommended by the European Committee on Antimicrobial susceptibility testing (EUCAST). Results are oriented towards speeding and facilitating epidemiological analysis.
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