2013
DOI: 10.1590/s0001-37652013005000045
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Nuclear entropy, angular second moment, variance and texture correlation of thymus cortical and medullar lymphocytes: Grey level co-occurrence matrix analysis

Abstract: Grey level co-occurrence matrix analysis (GLCM) is a well-known mathematical method for quantification of cell and tissue textural properties, such as homogeneity, complexity and level of disorder. Recently, it was demonstrated that this method is capable of evaluating fine structural changes in nuclear structure that otherwise are undetectable during standard microscopy analysis. In this article, we present the results indicating that entropy, angular second moment, variance, and texture correlation of lympho… Show more

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Cited by 32 publications
(27 citation statements)
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“…The performance of the setup was tested with a sample of 20-nm gold nanoparticles. Using the PT imaging data, we then analyzed the structural properties of MM and nevus cells to be compared using the GLCM method [31][32][33][34][35][36][37]). We calculated nine different parameters: ASM, contrast, correlation, entropy, IDM, homogeneity, prominence, shade, and variance.…”
Section: Introductionmentioning
confidence: 99%
“…The performance of the setup was tested with a sample of 20-nm gold nanoparticles. Using the PT imaging data, we then analyzed the structural properties of MM and nevus cells to be compared using the GLCM method [31][32][33][34][35][36][37]). We calculated nine different parameters: ASM, contrast, correlation, entropy, IDM, homogeneity, prominence, shade, and variance.…”
Section: Introductionmentioning
confidence: 99%
“…For example, the GLMC method has been used to study the distribution arrangements of cells in two areas of the mouse brain, the cortex and the medulla. 14,15 However, GLCM-type analysis has not been utilized to study high resolution intracellular images.…”
Section: Introductionmentioning
confidence: 99%
“…The human quest for finding the image textural features dates back to 1970's when Haralick [1], Rosenfeld and Troy [2] have obtained textural coarseness of digital images by finding the difference of the gray values of the adjacent pixels and then performing autocorrelation of the image values. The texture based properties of digital images have also been used in medical images [3] and in tomography based images [4], analysis of ultrasound images [5] and classification of food items like Italian pasta and plum cakes [6,7].…”
Section: Introductionmentioning
confidence: 99%