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2017
DOI: 10.1117/1.jbo.22.3.036011
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Gray-level co-occurrence matrix analysis of several cell types in mouse brain using resolution-enhanced photothermal microscopy

Abstract: , "Gray-level co-occurrence matrix analysis of several cell types in mouse brain using resolution-enhanced photothermal microscopy," J. Biomed. Opt. 22(3), 036011 (2017), doi: 10.1117/1.JBO.22.3.036011. Abstract. Qualifications of intracellular structure were performed for the first time using the gray-level co-occurrence matrix (GLCM) method for images of cells obtained by resolution-enhanced photothermal imaging. The GLCM method has been used to extract five parameters of texture features for five different … Show more

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Cited by 9 publications
(4 citation statements)
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“…Second-order statistics study the texture through the spatial relationship between pairs of pixels [ 25 , 39 , 40 ]. To obtain these values, the spatial co-occurrence between pixel pairs is gathered through the gray-level co-occurrence matrix (GLCM) [ 41 ]. From the GLCM, up to fourteen parameters can be calculated to measure texture [ 25 , 40 ].…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Second-order statistics study the texture through the spatial relationship between pairs of pixels [ 25 , 39 , 40 ]. To obtain these values, the spatial co-occurrence between pixel pairs is gathered through the gray-level co-occurrence matrix (GLCM) [ 41 ]. From the GLCM, up to fourteen parameters can be calculated to measure texture [ 25 , 40 ].…”
Section: Methodsmentioning
confidence: 99%
“…However, only seven provide essential information [ 39 ]. Hence, the used second-order statistics were: entropy, contrast, homogeneity, dissimilarity, angular second moment (ASM), energy, and correlation [ 25 , 41 ].…”
Section: Methodsmentioning
confidence: 99%
“…Each block, across all images, was analysed for texture by using a modi ed method of extracting Haralick features from the Gray Level Co-Occurrence Matrix (GLCM) [19]. LCM based texture measures provide information related to the local spatial relationships of gray levels in an image and have previously been successfully applied in a number of medical imaging modalities [19][20][21][22][23]. One of the challenges in the standard application of the GLCM is the dependence on rotation and spatial offset, which is pertinent to this study.…”
Section: Texture Analysismentioning
confidence: 99%
“…Gray level co-occurrence matrix [23] is a statistical method for extracting texture features from digital images through calculating the spatial relationship of each image pixel. The implemented method is based on the joint conditional probability density among image grayscale levels, whose function is:…”
Section: Gray Level Co-occurrence Matrixmentioning
confidence: 99%