Discriminatory ability of fractal and grey level co-occurrence matrix methods in structural analysis of hippocampus layers
2015
Autori:
Pantic, IgorDacic, Sanja
Brkic, Predrag
Lavrnja, Irena
Jovanovic, Tomislav
Pantic, Senka
Peković, Sanja
Tip dokumenta:
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt:
Fractal and grey level co-occurrence matrix (GLCM) analysis represent
two mathematical computer-assisted algorithms that are today thought to
be able to accurately detect and quantify changes in tissue architecture
during various physiological and pathological processes. However,
despite their numerous applications in histology and pathology, their
sensitivity, specificity and validity regarding evaluation of brain
tissue remain unclear. In this article we present the results indicating
that certain parameters of fractal and GLCM analysis have high
discriminatory ability in distinguishing two morphologically similar
regions of rat hippocampus: stratum lacunosum-moleculare and stratum
radiatum. Fractal and GLCM algorithms were performed on a total of 240
thionine-stained hippocampus micrographs of 12 male Wistar albino rats.
120 digital micrographs represented stratum lacunosum-moleculare, and
another 120 stratum radiatum. For each image, 7 parameters were
calculated: fractal dimension, lacunarity, GLCM angular second moment,
GLCM contrast, inverse difference moment, GLCM correlation, and GLCM
variance. GLCM variance (VAR) resulted in the largest area under the
Receiver operating characteristic (ROC) curve of 0.96, demonstrating an
outstanding discriminatory power in analysis of stratum
lacunosum-moleculare (average VAR equaled 478.1 +/- 179.8) and stratum
radiatum (average VAR of 145.9 +/- 59.2, p <0.0001). For the criterion
VAR <= 227.5, sensitivity and specificity were 90\% and 86.7\%,
respectively. GLCM correlation as a parameter also produced large area
under the ROC curve of 0.95. Our results are in accordance with the
findings of our previous study regarding brain white mass fractal and
textural analysis. GLCM algorithm as an image analysis method has
potentially high applicability in structural analysis of brain tissue
cytoarcitecture. (C) 2015 Elsevier Ltd. All rights reserved.
Ključne reči:
Texture; Variance; Boundary; ImageIzvor:
Journal of Theoretical Biology, 2015, 370, 151-156
DOI: 10.1016/j.jtbi.2015.01.035
ISSN: 1095-8541