Close
  Indian J Med Microbiol
 

Figure 1: Example demonstrating texture analysis using the GLCM. A left parotid gland with Grade 1 findings was examined with US, wherein mild inhomogeneity without anechoic area was found (a). The images containing maximized parenchymal area would then be selected for analysis (a, dotted-line rectangle). After cropping the image into a rectangular shape, the raster graphics were converted to a matrix (b). Subsequently, GLCMs could be created with the R package GLCMTextures. Eight GLCM metrics were then explored and adopted in this study: CON, DIS, HOM, ASM, ENT, mean, VAR, and COR. The textural features were calculated in all four directions (0°, 45°, 90°, and 135°), and were then combined to one rotation-invariant texture (c). GLCM: Gray-level co-occurrence matrix, US: Ultrasound, CON: Contrast, DIS: Dissimilarity, HOM: Homogeneity, ASM: Angular second moment, ENT: Entropy, VAR: Variance, COR: Correlation

Figure 1: Example demonstrating texture analysis using the GLCM. A left parotid gland with Grade 1 findings was examined with US, wherein mild inhomogeneity without anechoic area was found (a). The images containing maximized parenchymal area would then be selected for analysis (a, dotted-line rectangle). After cropping the image into a rectangular shape, the raster graphics were converted to a matrix (b). Subsequently, GLCMs could be created with the R package GLCMTextures. Eight GLCM metrics were then explored and adopted in this study: CON, DIS, HOM, ASM, ENT, mean, VAR, and COR. The textural features were calculated in all four directions (0°, 45°, 90°, and 135°), and were then combined to one rotation-invariant texture (c). GLCM: Gray-level co-occurrence matrix, US: Ultrasound, CON: Contrast, DIS: Dissimilarity, HOM: Homogeneity, ASM: Angular second moment, ENT: Entropy, VAR: Variance, COR: Correlation