Automatic Classification of Liquid Crystal Images Based on Topological Analysis

• Published in: IEEE sensors journal Vol. 23; no. 2; pp. 1377 - 1388
• Main Author: Romanov, Alexey M.
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Biosensor; Biosensors; classification; Feature extraction; graphics processing unit (GPU); Image classification; image processing; Image segmentation; liquid crystal; Liquid crystals; Machine learning; Microscopy; Optical sensors; Parameters; pattern recognition; Quality assessment; sensor data processing; Sensors; Support vector machines; Topology; Water quality
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2022.3225584

Liquid crystal image classification is a vital part of modern biosensors. Recent studies have shown that this problem can be solved using computationally intensive intelligent analysis tools, such as convolutional neural networks (CNNs) and support vector machines (SVMs). This article proposes a new method of microscopic image classification for liquid crystals-based biosensors with fast response. This method is based on topological analysis and provides 95% accuracy. Moreover, on the same hardware, it reaches eightfold performance compared to CNNs, which are usually used in similar applications. Finally, it has only nine parameters. Most of those parameters are independent and can be easily tuned based on the properties of the liquid crystals suspension and the microscope. This is a significant benefit compared to machine learning approaches that require large training datasets. The proposed solution can be considered a new step toward the creation of fully automatic biosensors for industrial water quality assessment systems.