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

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.

Machine Learning-Based Gray-Level Co-Occurrence Matrix (GLCM) Models for Predicting the Depth of Myometrial Invasion in Patients with Stage I Endometrial Cancer

“… 25 , 26 GLCM analysis is a contemporary and innovative computational method to assess the textural patterns applicable to most areas of microscopy. 27 To the best of our knowledge, this is the first study to create a relatively sensitive GLCM-based ML model for evaluating DMI. Based on the obtained GLCM data, we applied five ML approaches.…”

Analysis of Vascular Architecture and Parenchymal Damage Generated by Reduced Blood Perfusion in Decellularized Porcine Kidneys Using a Gray Level Co-occurrence Matrix

“…This GLCM feature indicates textural homogeneity and is often used to quantify smoothness in the distribution of resolution units in grayscale images. Previous research articles in digital micrographs have shown the potential value of inverse difference moment in detecting structural alterations that are not visible to a professional pathologist ( 35 , 44 ).…”

“…In the future, probably the most important application of both GLCM and DWT analyses will be to provide inputs for various artificial intelligence-based methods for image analysis in radiology. This application would include training and testing different machine learning models, some of which have already been suggested as suitable for GLCM data ( 44 ). The examples would be conventional decision tree algorithms such as CHAID (Chi-square Automatic Interaction Detector) or CART (classification and regression tree) or some more modern approaches such as random forests.…”

Gray level co-occurrence matrix and wavelet analyses reveal discrete changes in proximal tubule cell nuclei after mild acute kidney injury

“…In our previous articles, we applied similar GLCM approaches to analyze alterations to the renal vascular architecture 22 , highlighting its potential application in the characterization of whole organ scaffolds 23 that can be generated for bioarti cial kidney development 24 . Using this technique, We also examined cell nuclei after the damage induced by exposure to a sublethal toxic dose of ethanol 12 . On an experimental model of saccharomyces cerevisiae, we calculated angular second moment, inverse difference moment, textural contrast, GLCM correlation, and variance and demonstrated that these features signi cantly change after alcohol treatment.…”

“…The second technique is based on the discrete wavelet transform (DWT), a mathematical approach to the texture frequently applied for two-dimensional signal analysis as an addition to GLCM. Previous research has shown that both methods are potentially valuable tools in pathology for differentiating damaged and intact cells 11,12 . Also, both methods can be used to train and develop arti cial intelligence (AI) machine learning models, such as those based on decision trees, logistic regression, or arti cial neural networks.…”