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
“…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.…”
Label-free confocal photothermal (CPT) microscopy was utilized for the first time to investigate malignancy in mouse skin cells. A laser diode (LD) with 405nm or 488nm was used as a pump and 638nm LD as a probe for the CPT microscope. The Grey Level Cooccurrence Matrix (GLCM) for texture analysis was applied to the CPT images. Nine parameters of GLCM were calculated for the intracellular super-resolved CPT images, and the parameters Entropy and Prominence were found to be most suited among the nine parameters to discriminate between healthy cells and MM cells in case pump wavelength of 488nm is used.
“…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.…”
Label-free confocal photothermal (CPT) microscopy was utilized for the first time to investigate malignancy in mouse skin cells. A laser diode (LD) with 405nm or 488nm was used as a pump and 638nm LD as a probe for the CPT microscope. The Grey Level Cooccurrence Matrix (GLCM) for texture analysis was applied to the CPT images. Nine parameters of GLCM were calculated for the intracellular super-resolved CPT images, and the parameters Entropy and Prominence were found to be most suited among the nine parameters to discriminate between healthy cells and MM cells in case pump wavelength of 488nm is used.
“…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.…”
“…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].…”
Abstract-This paper proposes to improve the classification accuracy of the leaf images by extracting texture and statistical features by utilizing the presence of striking features on the dorsal and ventral sides of the leaves, which on other types of objects may not be that prominent. The texture features have been extracted from dorsal, ventral and a combination of dorsalventral sides of leaf images using Gray level co-occurrence matrix. In addition to this, this work also uses certain general statistical features for discriminating them into various classes. The feature selection work has been performed separately for the dorsal, ventral and combined data sets (for both texture and statistical features) using the most common feature selection algorithms.After selecting the relevant features, the classification has been done using the classification algorithms: K-Nearest Neighbor, J48, Naïve Bayes, Partial Least Square (PLS), Classification and Regression Tree (CART), Classification Tree(CT). The classification accuracy has been calculated and compared to find which side of the leaf image (dorsal or ventral) gives better results with which type of features(texture or statistical). This study reveals that the ventral leaf features can be another alternative in discriminating the leaf images into various classes.
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