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Sensors
Volume 25
Issue 11
10.3390/s25113462
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Classification of Microbial Activity and Inhibition Zones Using Neural Network Analysis of Laser Speckle Images

by
Ilya Balmages
1,*,
Dmitrijs Bļizņuks
1,
Inese Polaka
2,
Alexey Lihachev
3 and
Ilze Lihacova
3
1
Institute of Applied Computer Systems, Riga Technical University, LV-1048 Riga, Latvia
2
Institute of Information Technology, Riga Technical University, LV-1048 Riga, Latvia
3
Institute of Atomic Physics and Spectroscopy, Faculty of Science and Technology, University of Latvia, LV-1040 Riga, Latvia
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(11), 3462; https://doi.org/10.3390/s25113462 (registering DOI)
Submission received: 29 April 2025 / Revised: 23 May 2025 / Accepted: 28 May 2025 / Published: 30 May 2025
(This article belongs to the Section Intelligent Sensors)
Review Reports Versions Notes

Abstract

This study addresses the challenge of rapidly and accurately distinguishing zones of microbial activity from antibiotic inhibition zones in Petri dishes. We propose a laser speckle imaging technique enhanced with subpixel correlation analysis to monitor dynamic changes in the inhibition zone surrounding an antibiotic disc. This method provides faster results compared to the standard disk diffusion assay recommended by EUCAST. To enable automated analysis, we used machine learning algorithms for classifying areas of bacterial or fungal activity versus inhibited growth. Classification is performed over short time windows (e.g., 1 h), supporting near-real-time assessment. To further improve accuracy, we introduce a correction method based on the known spatial dynamics of inhibition zone formation. The novelty of the study lies in combining a speckle imaging subpixel correlation algorithm with ML classification and with pre- and post-processing. This approach enables early automated assessment of antimicrobial effects with potential applications in rapid drug susceptibility testing and microbiological research.
Keywords: laser speckle imaging; correlation analysis; image processing; signal processing; microorganism spatiotemporal activity estimation; classification of microorganism’s activity; artificial neural networks laser speckle imaging; correlation analysis; image processing; signal processing; microorganism spatiotemporal activity estimation; classification of microorganism’s activity; artificial neural networks

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MDPI and ACS Style

Balmages, I.; Bļizņuks, D.; Polaka, I.; Lihachev, A.; Lihacova, I. Classification of Microbial Activity and Inhibition Zones Using Neural Network Analysis of Laser Speckle Images. Sensors 2025, 25, 3462. https://doi.org/10.3390/s25113462

AMA Style

Balmages I, Bļizņuks D, Polaka I, Lihachev A, Lihacova I. Classification of Microbial Activity and Inhibition Zones Using Neural Network Analysis of Laser Speckle Images. Sensors. 2025; 25(11):3462. https://doi.org/10.3390/s25113462

Chicago/Turabian Style

Balmages, Ilya, Dmitrijs Bļizņuks, Inese Polaka, Alexey Lihachev, and Ilze Lihacova. 2025. "Classification of Microbial Activity and Inhibition Zones Using Neural Network Analysis of Laser Speckle Images" Sensors 25, no. 11: 3462. https://doi.org/10.3390/s25113462

APA Style

Balmages, I., Bļizņuks, D., Polaka, I., Lihachev, A., & Lihacova, I. (2025). Classification of Microbial Activity and Inhibition Zones Using Neural Network Analysis of Laser Speckle Images. Sensors, 25(11), 3462. https://doi.org/10.3390/s25113462

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