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.

Application of Gray Level co-Occurrence Matrix Algorithm for Detection of Discrete Structural Changes in Cell Nuclei After Exposure to Iron Oxide Nanoparticles and 6-Hydroxydopamine

2019 ◽  
Vol 25 (4) ◽  
pp. 982-988
Author(s):  
Dubravka Nikolovski ◽  
Jelena Cumic ◽  
Igor Pantic
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Iron Oxide ◽  
Iron Oxide Nanoparticles ◽  
Structural Changes ◽  
Yeast Cells ◽  
Oxide Nanoparticles ◽  
Gray Level ◽  
Cell Nuclei ◽  
Occurrence Matrix ◽  
6 Hydroxydopamine

AbstractThe gray level co-occurrence matrix (GLCM) algorithm is a contemporary computational biology method which, today, is frequently used to detect small changes in texture that are not visible using conventional techniques. We demonstrate that the toxic compound 6-hydroxydopamine (6-OHDA) and iron oxide nanoparticles (IONPS) have opposite effects on GLCM features of cell nuclei. Saccharomyces cerevisiae yeast cells were treated with 6-OHDA and IONPs, and imaging with GLCM analysis was performed at three different time points: 30 min, 60 min, and 120 min after the treatment. A total of 200 cell nuclei were analyzed, and for each nucleus, 5 GLCM parameters were calculated: Angular second moment (ASM), Inverse difference moment (IDM), Contrast (CON), Correlation (COR) and Sum Variance (SVAR). Exposure to IONPs was associated with the increase of ASM and IDM while the values of SVAR and COR were reduced. Treatment with 6-OHDA was associated with the increase of SVAR and CON, while the values of nuclear ASM and IDM were reduced. This is the first study to indicate that IONPs and 6-OHDA have opposite effects on nuclear texture. Also, to the best of our knowledge, this is the first study to apply the GLCM algorithm in Saccharomyces cerevisiae yeast cells in this experimental setting.

scholarly journals The combination of gray level co-occurrence matrix and back propagation neural network for classifying stairs descent and floor

ICT Express ◽  
2021 ◽  
Author(s):  
Fitri Utaminingrum ◽  
Syam Julio A. Sarosa ◽  
Corina Karim ◽  
Femiana Gapsari ◽  
Randy Cahya Wihandika
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Gray Level ◽  
Occurrence Matrix

scholarly journals Efficient CU and PU Decision Based on Neural Network and Gray Level Co-Occurrence Matrix for Intra Prediction of Screen Content Coding

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 46643-46655 ◽  
Author(s):  
Chao Huang ◽  
Zongju Peng ◽  
Fen Chen ◽  
Qiuping Jiang ◽  
Gangyi Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Intra Prediction ◽  
Gray Level ◽  
Screen Content ◽  
Occurrence Matrix ◽  
Screen Content Coding

scholarly journals Deteksi Api Pada Video Menggunakan Metode Artificial Neural Network

2021 ◽  
Vol 3 (1) ◽  
pp. 96-107
Author(s):  
Budiman Rabbani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Extraction Method ◽  
Gray Level ◽  
Backpropagation Neural Network ◽  
Validation Process ◽  
Feature Extraction Method ◽  
Time Value ◽  
Occurrence Matrix

Abstract The camera is one of the tools used to collect images. Cameras are often used for the safety of homes, highways and others. Then in this study camera captures are used to support fire objects because fire is one of the causes of safety that can be controlled. Therefore, by utilizing a capture camera will see the best model of backpropagation neural network by combining the local binary patern (LBP) feature extraction method and the Gray Level Co-occurrence Matrix (GLCM) to access the fire. Then to evaluate the performance of the model created using three parameters that contain accuracy, recall, precision. The data in this study consisted of videos with variations of fire and non-fire videos. Based on the final results of the study, accuracy, remember, the best precision obtained simultaneously 96%, 97%, 97%. Then the validation process was done using 30 videos with a variation of 15 fire videos and 15 non-fire videos and obtained an accuracy of 86.6% with an average time value of 6.029 minutes.

scholarly journals CLASSIFICATION OF MEAT IMAGERY USING ARTIFICIAL NEURAL NETWORK METHOD AND TEXTURE FEATURE EXTRACTION BY GRAY LEVEL CO-OCCURRENCE MATRIX METHOD

2021 ◽  
Vol 2 (1) ◽  
pp. 1-8
Author(s):  
Chairul Imam ◽  
Eka Wahyu Hidayat ◽  
Neng Ika Kurniati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Texture Feature ◽  
General Term ◽  
Gray Level ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Occurrence Matrix

Lately, there is often a mixture of beef and pork done by traders to the general public as buyers. This is due to the unconsciousness of the buyer on how to recognize the type of meat purchased. The effect of this meat mix can certainly be detrimental to buyers, especially Muslims. Image processing is a general term for various methods in which it is used to manipulate and modify images in various ways. Classification is a method of grouping some information and ensuring it is listed in a class.. Classification of beef and pork differentiator in this application using Artificial Neural Network (ANN) method while for texture extraction using Gray Level Co-occurrence Matrix (GLCM) method. The information used in the examination was 30 images of fresh meat divided into 15 images of fresh beef and 15 images of fresh pork. The data used is data Classification of Beef and Pork Image based on Color and Texture Characteristics. The result of classification accuracy obtained in this application is 80%.

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