Please use this identifier to cite or link to this item: https://biore.bio.bg.ac.rs/handle/123456789/1001
Title: Application of Gray Level Co-Occurrence Matrix Analysis as a New Method for Enzyme Histochemistry Quantification
Authors: Dragić, Milorad 
Zarić, Marina
Mitrović, Nataša
Nedeljković, Nadežda 
Grković, Ivana
Keywords: ecto-5′-nucleotidase;enzyme histochemistry;gray-level co-occurrence matrix;pathology;texture analysis
Issue Date: 1-Jan-2019
Rank: M21
Journal: Microscopy and Microanalysis
Abstract: 
© 2019 Microscopy Society of America. Enzyme 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.
URI: https://biore.bio.bg.ac.rs/handle/123456789/1001
ISSN: 1431-9276
DOI: 10.1017/S1431927618016306
Appears in Collections:Journal Article

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