Computing Gray-Level Co-occurrence Matrix for Images

Compute the gray-level co-occurrence matrix (GLCM) of images and extract texture features. The GLCM is a fundamental tool for characterizing image texture patterns by quantifying the spatial relationships between pixel intensity values. This matrix captures texture information by analyzing how frequently pairs of pixels with specific gray-level values occur at a given spatial offset. Common texture features derived from GLCM include contrast (measuring local intensity variations), energy (representing uniformity), entropy (quantifying randomness), and correlation (assessing linear dependencies). Through texture feature analysis, we can obtain valuable information about image structure and organizational patterns. In implementation, the GLCM calculation typically involves defining displacement parameters (distance and angle), then counting co-occurring pixel pairs across the image. The extraction of texture features subsequently applies mathematical formulas to the normalized GLCM. Therefore, computing GLCM and extracting texture features is crucial for image processing and analysis tasks such as pattern recognition, medical imaging, and remote sensing applications.