Association between Chromatin Structural Organization of Peripheral Blood Neutrophils and Self-Perceived Mental Stress: Gray-Level Co-occurrence Matrix Analysis

2021 ◽  
pp. 1-7
Author(s):  
Nikola Topalovic ◽  
Sanja Mazic ◽  
Dejan Nesic ◽  
Olivera Vukovic ◽  
Jelena Cumic ◽  
...  
Keyword(s):  
Peripheral Blood ◽  
Mental Stress ◽  
Structural Organization ◽  
Matrix Analysis ◽  
Gray Level ◽  
Chromatin Structural ◽  
Blood Neutrophils ◽  
Occurrence Matrix

Abstract

Effects of Iron Oxide Nanoparticles on Structural Organization of Hepatocyte Chromatin: Gray Level Co-occurrence Matrix Analysis

2021 ◽  
pp. 1-8
Author(s):  
Jovana Paunovic ◽  
Danijela Vucevic ◽  
Tatjana Radosavljevic ◽  
Biserka Vukomanovic Djurdjevic ◽  
Sanja Stankovic ◽  
...  
Keyword(s):  
Iron Oxide ◽  
Iron Oxide Nanoparticles ◽  
Structural Organization ◽  
Matrix Analysis ◽  
Oxide Nanoparticles ◽  
Gray Level ◽  
Occurrence Matrix

Abstract

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

2021 ◽  
pp. 1-7
Author(s):  
Lazar M. Davidovic ◽  
Jelena Cumic ◽  
Stefan Dugalic ◽  
Sreten Vicentic ◽  
Zoran Sevarac ◽  
...  
Keyword(s):  
Neural Network ◽  
Saccharomyces Cerevisiae ◽  
Random Trees ◽  
Computational Method ◽  
Yeast Cells ◽  
Matrix Analysis ◽  
Gray Level ◽  
Cell Nuclei ◽  
Intact Cells ◽  
Occurrence Matrix

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.

Gray-Level Co-Occurrence Matrix Analysis of Granule Neurons of the Hippocampal Dentate Gyrus Following Cortical Injury

2020 ◽  
Vol 26 (1) ◽  
pp. 166-172
Author(s):  
Igor Pantic ◽  
Rada Jeremic ◽  
Sanja Dacic ◽  
Sanja Pekovic ◽  
Senka Pantic ◽  
...  
Keyword(s):  
Dentate Gyrus ◽  
Sensorimotor Cortex ◽  
Matrix Analysis ◽  
Granule Neurons ◽  
Gray Level ◽  
Hippocampal Dentate Gyrus ◽  
Memory Impairments ◽  
Highly Sensitive ◽  
Occurrence Matrix ◽  
The Impact

AbstractTraumatic brain injury (TBI) is a main cause of death and disabilities in young adults. Although learning and memory impairments are a major clinical manifestation of TBI, the consequences of TBI on the hippocampus are still not well understood. In particular, how lesions to the sensorimotor cortex damage the hippocampus, to which it is not directly connected, is still elusive. Here, we study the effects of sensorimotor cortex ablation (SCA) on the hippocampal dentate gyrus, by applying a highly sensitive gray-level co-occurrence matrix (GLCM) analysis. Using GLCM analysis of granule neurons, we discovered, in our TBI paradigm, subtle changes in granule cell (GC) morphology, including textual uniformity, contrast, and variance, which is not detected by conventional microscopy. We conclude that sensorimotor cortex trauma leads to specific changes in the hippocampus that advance our understanding of the cellular underpinnings of cognitive impairments in TBI. Moreover, we identified GLCM analysis as a highly sensitive method to detect subtle changes in the GC layers that is expected to significantly improve further studies investigating the impact of TBI on hippocampal neuropathology.

scholarly journals Recent Deforestation Pattern Changes (2000–2017) in the Central Carpathians: A Gray-Level Co-Occurrence Matrix and Fractal Analysis Approach

Forests ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 308 ◽  
Author(s):  
Ana-Maria Ciobotaru ◽  
Ion Andronache ◽  
Helmut Ahammer ◽  
Herbert F. Jelinek ◽  
Marko Radulovic ◽  
...  
Keyword(s):  
Fractal Analysis ◽  
Sustainable Forest Management ◽  
Matrix Analysis ◽  
Tree Cover ◽  
Gray Level ◽  
Forest Change ◽  
Landsat 7 ◽  
Occurrence Matrix ◽  
Central Carpathians ◽  
Fractal Fragmentation

The paper explores the distribution of tree cover and deforested areas in the Central Carpathians in the central-east part of Romania, in the context of the anthropogenic forest disturbances and sustainable forest management. The study aims to evaluate the spatiotemporal changes in deforested areas due to human pressure in the Carpathian Mountains, a sensitive biodiverse European ecosystem. We used an analysis of satellite imagery with Landsat-7 Enhanced Thematic Mapper Plus (Landsat-7 ETM+) from the University of Maryland (UMD) Global Forest Change (GFC) dataset. The workflow started with the determination of tree cover and deforested areas from 2000–2017, with an overall accuracy of 97%. For the monitoring of forest dynamics, a Gray-Level Co-occurrence Matrix analysis (Entropy) and fractal analysis (Fractal Fragmentation-Compaction Index and Tug-of-War Lacunarity) were utilized. The increased fragmentation of tree cover (annually 2000–2017) was demonstrated by the highest values of the Fractal Fragmentation-Compaction Index, a measure of the degree of disorder (Entropy) and heterogeneity (Lacunarity). The principal outcome of the research reveals the dynamics of disturbance of tree cover and deforested areas expressed by the textural and fractal analysis. The results obtained can be used in the future development and adaptation of forestry management policies to ensure sustainable management of exploited forest areas.

scholarly journals Application of Fractal and Gray-Level Co-Occurrence Matrix Indices to Assess the Forest Dynamics in the Curvature Carpathians—Romania

2019 ◽  
Vol 11 (24) ◽  
pp. 6927 ◽  
Author(s):  
Ana-Maria Ciobotaru ◽  
Ion Andronache ◽  
Helmut Ahammer ◽  
Marko Radulovic ◽  
Daniel Peptenatu ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Fractal Analysis ◽  
Forest Dynamics ◽  
Agricultural Land ◽  
Probability Distributions ◽  
Quantitative Information ◽  
Matrix Analysis ◽  
Gray Level ◽  
Occurrence Matrix ◽  
Matrix Evolution

The mountain ecosystems face significant damage from deforestation and environmental forest changes. We investigated the evolution of tree types of cover areas, deforested areas and total deforested areas from Curvature Carpathians using Gray-Level Co-occurrence Matrix and fractal analysis. The forest dynamics mapping was one of the main objectives of this study and it was carried out using multiple fractal and GLCM indices. We approached the analysis of satellite forest images by calculation of four fractal indices such as Pyramid dimension, Cube Counting Dimension, Fractal Fragmentation-Compaction Index and Tug-of-War lacunarity. We also calculated fractal dimension because it is an index of complexity comparing how the detail in a pattern changes with the scale at which it is measured. Fractal dimension is useful for estimation of irregularity or roughness of fractal and natural objects that do not conform to Euclidian geometry. While the fractal dimension quantifies how much space is occupied, the Tug-of-War lacunarity complements fractal dimension with its ability to quantify how space is occupied. Analysis was further supplemented by the Gray-Level Co-occurrence Matrix analysis because it quantifies spatial probability distributions of gray level values between pixel pairs within an image. The calculated Gray-Level Co-occurrence Matrix features included Angular Second Moment, Contrast, Correlation, Inverse Difference Moment and Entropy. Such comprehensive analysis has the advantage of combining fractal analysis that extracts quantitative information about the morphological complexity of the image with the spatial distribution of the gray pixel intensities as calculated by the co-occurrence features provided by Gray-Level Co-occurrence Matrix. Evolution of deforested areas, expansion of agricultural land and the increased demand for quality timber have affected the forests ecosystems and, the regional sustainable development of local communities.

Application of Gray Level Co-Occurrence Matrix Analysis as a New Method for Enzyme Histochemistry Quantification

2019 ◽  
Vol 25 (3) ◽  
pp. 690-698 ◽  
Author(s):  
Milorad Dragić ◽  
Marina Zarić ◽  
Nataša Mitrović ◽  
Nadežda Nedeljković ◽  
Ivana Grković
Keyword(s):  
Texture Analysis ◽  
Enzyme Histochemistry ◽  
Cell Types ◽  
Matrix Analysis ◽  
Qualitative Assessment ◽  
Specific Cell ◽  
Gray Level ◽  
Characteristic Analysis ◽  
Occurrence Matrix

AbstractEnzyme histochemistry is a valuable histological method which provides a connection between morphology, activity, and spatial localization of investigated enzymes. Even though the method relies purely on arbitrary evaluations performed by the human eye, it is still wildly accepted and used in histo(patho)logy. Texture analysis emerged as an excellent tool for image quantification of subtle differences reflected in both spatial discrepancies and gray level values of pixels. The current study of texture analysis utilizes the gray-level co-occurrence matrix as a method for quantification of differences between ecto-5′-nucleotidase activities in healthy hippocampal tissue and tissue with marked neurodegeneration. We used the angular second moment, contrast (CON), correlation, inverse difference moment (INV), and entropy for texture analysis and receiver operating characteristic analysis with immunoblot and qualitative assessment of enzyme histochemistry as a validation. Our results strongly argue that co-occurrence matrix analysis could be used for the determination of fine differences in the enzyme activities with the possibility to ascribe those differences to regions or specific cell types. In addition, it emerged that INV and CON are especially useful parameters for this type of enzyme histochemistry analysis. We concluded that texture analysis is a reliable method for quantification of this descriptive technique, thus removing biases and adding it a quantitative dimension.

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