Application of Fractal and Grey Level Co-Occurrence Matrix Analysis in Evaluation of Brain Corpus Callosum and Cingulum Architecture

Abstract

This aim of this study was to assess the discriminatory value of fractal and grey level co-occurrence matrix (GLCM) analysis methods in standard microscopy analysis of two histologically similar brain white mass regions that have different nerve fiber orientation. A total of 160 digital micrographs of thionine-stained rat brain white mass were acquired using a Pro-MicroScan DEM-200 instrument. Eighty micrographs from the anterior corpus callosum and eighty from the anterior cingulum areas of the brain were analyzed. The micrographs were evaluated using the National Institutes of Health ImageJ software and its plugins. For each micrograph, seven parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, GLCM variance, fractal dimension, and lacunarity. Using the Receiver operating characteristic analysis, the highest discriminatory value was determined for inverse difference moment (IDM) (area under the receiver operating characteristic (ROC) curve equaled 0.925, and for the criterion IDM≤0.610 the sensitivity and specificity were 82.5 and 87.5%, respectively). Most of the other parameters also showed good sensitivity and specificity. The results indicate that GLCM and fractal analysis methods, when applied together in brain histology analysis, are highly capable of discriminating white mass structures that have different axonal orientation.

Quantification of structural changes in acute inflammation by fractal dimension, angular second moment and correlation

“…In contrast, standard deviation of fractal dimension of the cells was found to be larger in inflammatory group than in control group. Based on results of previous studies on fractal and textural features of normal biological structures (Pantic et al ., ; Pantic et al ., ), we may speculate that in physiological conditions GLCM parameters tend to have a relatively higher degree of variation compared to fractal dimension. In a state of inflammation, this relationship might be inverse, which may be attributed to discrete structural changes in pathologically changed tissue.…”

Variable angle gray level co‐occurrence matrix analysis of T2 relaxation time maps reveals degenerative changes of cartilage in knee osteoarthritis: Oulu knee osteoarthritis study

“…From this matrix, various features, such as homogeneity or entropy, can be calculated and these features characterize the structural properties of the image. Outside of its original applications in aerial photography, texture analysis has been increasingly applied to different types of medical images …”

“…Outside of its original applications in aerial photography, 2 texture analysis has been increasingly applied to different types of medical images. [3][4][5][6][7][8][9][10][11][12][13][14][15][16] In recent years, several publications have investigated the possibilities of applying GLCM analysis to cartilage T 2 relaxation time maps in knee osteoarthritis (OA). [7][8][9][10][11][12][13][14][15][16] Texture analysis has been applied to assess longitudinal changes, [8][9][10][11][12][13] symptomatic OA, 8,9,11,14,15 cartilage lesions, 9,12 subjects at risk for OA, 10,12,16 and degeneration following anterior cruciate ligament injury.…”

Fractal and gray level co-occurrence matrix texture analysis of senescent and non-senescent deciduous teeth stem cells: A pilot study

“…New methods and modifications of fractal analysis are also being discovered [19], thus allowing this mathematical method to be applied in different areas of natural sciences. Unlike fractal analysis, gray level co-occurrence matrix (GLCM) texture analysis can be used in biomedical research to analyze texture features of histological images and also to quantify structural changes in cells and tissues [20,21].…”