Texture Extraction Using Gray-Level Co-occurrence Matrix and Gray-Level Gradient Co-occurrence Matrix

Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Gradient Co-occurrence Matrix (GLGCM) are widely used image texture analysis methods. These techniques perform mathematical computations on images to extract multiple texture feature parameters such as contrast, energy, entropy, and correlation. These parameters effectively characterize image texture patterns and are commonly applied in image classification, object detection, and image matching applications. In code implementation, GLCM calculation typically involves specifying pixel pair distances and directions, while GLGCM incorporates gradient information for enhanced texture discrimination. The algorithms work by statistically analyzing the spatial relationships between pixel intensity values and their gradients.

Furthermore, GLCM and GLGCM are particularly valuable in medical image texture analysis, including CT and MRI image processing. These methods enable quantitative texture extraction and analysis that assists physicians in making more accurate diagnoses and developing precise treatment plans. In practical implementation, these matrices can be computed using image processing libraries like MATLAB's Image Processing Toolbox or Python's scikit-image, where functions such as graycomatrix() and custom gradient calculations are employed to generate feature vectors for machine learning applications.