Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-10T09:16:00.635Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access

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
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adur, J, Carvalho, HF, Cesar, CL & Casco, VH (2014). Nonlinear optical microscopy signal processing strategies in cancer. Cancer Inform 13, 67–76.10.4137/CIN.S12419CrossRefGoogle ScholarPubMed
Atila, Ü, Baydilli, YY, Sehirli, E & Turan, MK (2019). Classification of DNA damages on segmented comet assay images using convolutional neural network. Comput Methods Programs Biomed 186, 105192.CrossRefGoogle ScholarPubMed
Auesukaree, C (2017). Molecular mechanisms of the yeast adaptive response and tolerance to stresses encountered during ethanol fermentation. J Biosci Bioeng 124(2), 133–142.CrossRefGoogle ScholarPubMed
Cao, W, Pomeroy, MJ, Gao, Y, Barish, MA, Abbasi, AF, Pickhardt, PJ & Liang, Z (2019). Multi-scale characterizations of colon polyps via computed tomographic colonography. Vis Comput Ind Biomed Art 2, 25.10.1186/s42492-019-0032-7CrossRefGoogle ScholarPubMed
Caspeta, L, Castillo, T & Nielsen, J (2015). Modifying yeast tolerance to inhibitory conditions of ethanol production processes. Front Bioeng Biotechnol 3, 184.10.3389/fbioe.2015.00184CrossRefGoogle ScholarPubMed
Choe, YH, Jung, DH, Park, JC, Kim, HY, Shin, SK, Lee, SK & Lee, YC (2021). Prediction model for bleeding after endoscopic submucosal dissection of gastric neoplasms from a high-volume center. J Gastroenterol Hepatol 36(8), 2217–2223.CrossRefGoogle ScholarPubMed
Davidovic, LM, Laketic, D, Cumic, J, Jordanova, E & Pantic, I (2021). Application of artificial intelligence for detection of chemico-biological interactions associated with oxidative stress and DNA damage. Chem Biol Interact 345, 109533.CrossRefGoogle ScholarPubMed
Dinčić, M, Todorović, J, Nešović Ostojić, J, Kovačević, S, Dunđerović, D, Lopičić, S, Spasić, S, Radojević-Škodrić, S, Stanisavljević, D & Ilic, AŽ (2020). The fractal and GLCM textural parameters of chromatin may be potential biomarkers of papillary thyroid carcinoma in Hashimoto's thyroiditis specimens. Microsc Microanal 26(4), 717–730.CrossRefGoogle ScholarPubMed
Feng, Q & Ding, Z (2020). MRI radiomics classification and prediction in Alzheimer's disease and mild cognitive impairment: A review. Curr Alzheimer Res 17(3), 297–309.10.2174/1567205017666200303105016CrossRefGoogle ScholarPubMed
Försch, S, Klauschen, F, Hufnagl, P & Roth, W (2021). Artificial intelligence in pathology. Dtsch Arztebl Int 118(12), 194–204.Google ScholarPubMed
Hohmann, T, Kessler, J, Vordermark, D & Dehghani, F (2020). Evaluation of machine learning models for automatic detection of DNA double strand breaks after irradiation using a γH2AX foci assay. PLoS ONE 15(2), e0229620.CrossRefGoogle ScholarPubMed
Hoshino, S, Takeuchi, M, Kawakubo, H, Matsuda, S, Mayanagi, S, Irino, T, Fukuda, K, Nakamura, R, Wada, N & Kitagawa, Y (2021). Usefulness of neutrophil to lymphocyte ratio at recurrence for predicting long-term outcomes in patients with recurrent esophageal squamous cell carcinoma. Ann Surg Oncol 28(6), 3001–3008.CrossRefGoogle ScholarPubMed
Imakubo, M, Takayama, J, Okada, H & Onami, S (2021). Statistical image processing quantifies the changes in cytoplasmic texture associated with aging in Caenorhabditis elegans oocytes. BMC Bioinformatics 22(1), 73.CrossRefGoogle ScholarPubMed
Khosravi, P, Kazemi, E, Imielinski, M, Elemento, O & Hajirasouliha, I (2018). Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine 27, 317–328.CrossRefGoogle ScholarPubMed
Kriegsmann, M, Casadonte, R, Maurer, N, Stoehr, C, Erlmeier, F, Moch, H, Junker, K, Zgorzelski, C, Weichert, W, Schwamborn, K, Deininger, SO, Gaida, M, Mechtersheimer, G, Stenzinger, A, Schirmacher, P, Hartmann, A, Kriegsmann, J & Kriegsmann, K (2020). Mass spectrometry imaging differentiates chromophobe renal cell carcinoma and renal oncocytoma with high accuracy. J Cancer 11(20), 6081–6089.CrossRefGoogle ScholarPubMed
Lee, HK, Kim, CH, Bhattacharjee, S, Park, HG, Prakash, D & Choi, HK (2021). A paradigm shift in nuclear chromatin interpretation: From qualitative intuitive recognition to quantitative texture analysis of breast cancer cell nuclei. Cytometry A 99(7), 698–706.10.1002/cyto.a.24260CrossRefGoogle ScholarPubMed
Liu, Z, Jin, L, Chen, J, Fang, Q, Ablameyko, S, Yin, Z & Xu, Y (2021). A survey on applications of deep learning in microscopy image analysis. Comput Biol Med 134, 104523.CrossRefGoogle ScholarPubMed
Mapayi, T, Viriri, S & Tapamo, JR (2015). Adaptive thresholding technique for retinal vessel segmentation based on GLCM-energy information. Comput Math Methods Med 2015, 597475.Google ScholarPubMed
McCombe, KD, Craig, SG, Viratham Pulsawatdi, A, Quezada-Marín, JI, Hagan, M, Rajendran, S, Humphries, MP, Bingham, V, Salto-Tellez, M, Gault, R & James, JA (2021). Histoclean: Open-source software for histological image pre-processing and augmentation to improve development of robust convolutional neural networks. Comput Struct Biotechnol J 19, 4840–4853.CrossRefGoogle ScholarPubMed
Nikolovski, D, Cumic, J & Pantic, I (2019). 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. Microsc Microanal 25, 982–988.CrossRefGoogle ScholarPubMed
Ou, X, Pan, W & Xiao, P (2014). In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM). Int J Pharm 460(1-2), 28–32.CrossRefGoogle Scholar
Pantic, I, Basailovic, M, Paunovic, J & Pantic, S (2015). Relationship between chromatin complexity and nuclear envelope circularity in hippocampal pyramidal neurons. Chaos, Solitons & Fractals 76, 271–277.10.1016/j.chaos.2015.04.009CrossRefGoogle Scholar
Pantic, I, Dimitrijevic, D, Nesic, D & Petrovic, D (2016). Gray level co-occurrence matrix algorithm as pattern recognition biosensor for oxidopamine-induced changes in lymphocyte chromatin architecture. J Theor Biol 406, 124–128.CrossRefGoogle ScholarPubMed
Pantic, I, Paunovic, J, Perovic, M, Cattani, C, Pantic, S, Suzic, S, Nesic, D & Basta-Jovanovic, G (2013). Time-dependent reduction of structural complexity of the buccal epithelial cell nuclei after treatment with silver nanoparticles. J Microsc 252, 286–294.10.1111/jmi.12091CrossRefGoogle ScholarPubMed
Parapouli, M, Vasileiadis, A, Afendra, AS & Hatziloukas, E (2020). Saccharomyces cerevisiae and its industrial applications. AIMS Microbiol 6(1), 1–31.10.3934/microbiol.2020001CrossRefGoogle ScholarPubMed
Paunovic, J, Vucevic, D, Radosavljevic, T, Pantic, S, Veskovic, M & Pantic, I (2019). Gray-level co-occurrence matrix analysis of chromatin architecture in periportal and perivenous hepatocytes. Histochem Cell Biol 151(1), 75–83.CrossRefGoogle ScholarPubMed
Santos, TA, Maistro, CE, Silva, CB, Oliveira, MS, Franca, M Jr & Castellano, G (2015). MRI texture analysis reveals bulbar abnormalities in friedreich ataxia. Am J Neuroradiol 36(12), 2214–2218.10.3174/ajnr.A4455CrossRefGoogle ScholarPubMed
Stanley, D, Bandara, A, Fraser, S, Chambers, PJ & Stanley, GA (2010). The ethanol stress response and ethanol tolerance of Saccharomyces cerevisiae. J Appl Microbiol 109(1), 13–24.CrossRefGoogle ScholarPubMed
Tan, J, Gao, Y, Liang, Z, Cao, W, Pomeroy, MJ, Huo, Y, Li, L, Barish, MA, Abbasi, AF & Pickhardt, PJ (2020). 3D-GLCM CNN: A 3-dimensional gray-level co-occurrence matrix-based CNN model for polyp classification via CT colonography. IEEE Trans Med Imaging 39(6), 2013–2024.CrossRefGoogle ScholarPubMed
Tan, TC, Ritter, LJ, Whitty, A, Fernandez, RC, Moran, LJ, Robertson, SA, Thompson, JG & Brown, HM (2016). Gray level co-occurrence matrices (GLCM) to assess microstructural and textural changes in pre-implantation embryos. Mol Reprod Dev 83(8), 701–713.CrossRefGoogle ScholarPubMed
Topalovic, N, Mazic, S, Nesic, D, Vukovic, O, Cumic, J, Laketic, D, Stasevic Karlicic, I & Pantic, I (2021). Association between chromatin structural organization of peripheral blood neutrophils and self-perceived mental stress: Gray-level co-occurrence matrix analysis. Microsc Microanal 2, 1–7.Google Scholar
Vamvakas, SS & Kapolos, J (2020). Factors affecting yeast ethanol tolerance and fermentation efficiency. World J Microbiol Biotechnol 36(8), 114.CrossRefGoogle ScholarPubMed
Wu, M, Zhong, X, Peng, Q, Xu, M, Huang, S, Yuan, J, Ma, J & Tan, T (2019). Prediction of molecular subtypes of breast cancer using BI-RADS features based on a “white box” machine learning approach in a multi-modal imaging setting. Eur J Radiol 114, 175–184.10.1016/j.ejrad.2019.03.015CrossRefGoogle Scholar