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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 types of cells in mouse brain; pyramidal neurons and glial cells in the basal nucleus (BGl), dentate gyrus granule cells, cerebellar Purkinje cells, and cerebellar granule cells. The parameters are correlation, contrast, angular second moment (ASM), inverse difference moment (IDM), and entropy for the images of cells of interest in a mouse brain. The parameters vary depending on the pixel distance taken in the analysis method. Based on the obtained results, we identified that the most suitable GLCM parameter is IDM for pyramidal neurons and BGI, granule cells in the dentate gyrus, Purkinje cells and granule cells in the cerebellum. It was also found that the ASM is the most appropriate for neurons in the basal nucleus.

Visualization of deep blood vessels in speckle images is an important task as it is used to analyze the dynamics of the blood flow and the health status of biological tissue. Laser speckle imaging is a wide-field optical technique to measure relative blood flow speed based on the local speckle contrast analysis. However, it has been reported that this technique is limited to certain deep blood vessels (about ? = 300 ?? ? m ) because of the high scattering of the sample; beyond this depth, the quality of the vessel’s image decreases. The use of a representation based on homogeneity values, computed from the co-occurrence matrix, is proposed as it provides an improved vessel definition and its corresponding diameter. Moreover, a methodology is proposed for automatic blood vessel location based on the kurtosis analysis. Results were obtained from the different skin phantoms, showing that it is possible to identify the vessel region for different morphologies, even up to 900 ?? ? m in depth.

Distribution of scattered image patterns hinges on morphological and optical characteristics of cells. This paper applied a numerical method to simulate scattered images of real cell morphologies, which were reconstructed from confocal image stacks dyed by fluorescent stains. Two approaches, contourlet transform (CT) and gray level co-occurrence matrix (GLCM), were then used to analyze the simulated scattered images. The results showed that features extracted using GLCM contained more information than those extracted using CT. Higher classification accuracy could be achieved with a single GLCM parameter than CT and GLCM could achieve higher accuracy with fewer parameters than CT when using multiple parameters. Meanwhile, GLCM requires less computational cost. Thus, GLCM is more suitable and efficient than CT for the analysis of cell-scattered images.

Collagen is the most prominent protein in the human body, making up 30% of the total protein content. Quantitative studies have shown structural differences between collagen fibers of the normal and diseased tissues, due to the remodeling of the extracellular matrix during the pathological process. The dominant orientation, which is an important characteristic of collagen fibers, has not been taken into consideration for quantitative collagen analysis. Based on the conventional gray level co-occurrence matrix (GLCM) method, the authors proposed the orientation-dependent GLCM (OD-GLCM) method by estimating the dominant orientation of collagen fibers. The authors validated the utility of the OD-GLCM method on second harmonic generation (SHG) microscopic images of tendons from rats with different ages. Compared with conventional GLCM method, the authors' method has not only improved the discrimination between different tissues but also provided additional texture information of the orderliness of collagen fibers and the fiber size. The OD-GLCM method was further applied to the differentiation of the preliminary SHG images of normal and cancerous human pancreatic tissues. The combination of SHG microscopy and the OD-GLCM method might be helpful for the evaluation of diseases marked with abnormal collagen morphology.