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Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach

Published online by Cambridge University Press:  23 December 2021

Lazar M. Davidovic ,
Jelena Cumic ,
Stefan Dugalic ,
Sreten Vicentic ,
Zoran Sevarac ,
Georg Petroianu ,
Peter Corridon  and
Igor Pantic Open the ORCID record for Igor Pantic [Opens in a new window]
Lazar M. Davidovic
Affiliation:
University of Belgrade, Studentski trg 1, RS-11000Belgrade, Serbia
Jelena Cumic
Affiliation:
University of Belgrade, Faculty of Medicine, University Clinical Center of Serbia, Dr. Koste Todorovica 8, RS-11129 Belgrade, Serbia
Stefan Dugalic
Affiliation:
University of Belgrade, Faculty of Medicine, University Clinical Center of Serbia, Dr. Koste Todorovica 8, RS-11129 Belgrade, Serbia
Sreten Vicentic
Affiliation:
University of Belgrade, Faculty of Medicine, University Clinical Center of Serbia, Clinic of Psychiatry, Pasterova 2, RS-11000 Belgrade, Serbia
Zoran Sevarac
Affiliation:
University of Belgrade, Faculty of Organizational Sciences, Jove Ilica 154, RS-11000 Belgrade, Serbia
Georg Petroianu
Affiliation:
Department of Pharmacology & Therapeutics, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE
Peter Corridon
Affiliation:
Department of Immunology and Physiology, College of Medicine and Health Sciences; Biomedical Engineering, Healthcare Engineering Innovation Center; Center for Biotechnology; Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE
Igor Pantic*
Affiliation:
University of Belgrade, Faculty of Medicine, Department of Medical Physiology, Laboratory for Cellular Physiology, Visegradska 26/II, RS-11129 Belgrade, Serbia University of Haifa, 199 Abba Hushi Blvd. Mount Carmel, HaifaIL-3498838, Israel
*
*Corresponding author: Igor Pantic, E-mail: igor.pantic@med.bg.ac.rs; igorpantic@gmail.com
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Abstract

Gray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei.

Keywords

cellmicroscopymorphologynucleustexture
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 28 , Issue 1 , February 2022 , pp. 265 - 271
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

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