Extraction Methods for 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 important texture feature extraction methods. The extraction of these matrices involves analyzing and comparing pixel values within an image. Specifically, GLCM is calculated by comparing the gray-level values of each pixel with its adjacent pixels' gray-level values, typically using spatial relationships defined by offset parameters (distance and angle). On the other hand, GLGCM is computed by comparing the gray-level gradients of each pixel with the gradients of neighboring pixels, which incorporates both intensity and edge information. These matrix extraction processes can be implemented using software like MATLAB. During implementation, image preprocessing steps such as smoothing (using filters like Gaussian or median) and binarization may be required. MATLAB's Image Processing Toolbox provides key functions like graycomatrix() for GLCM calculation, where parameters including the number of gray levels, offset directions, and symmetric processing can be configured. For GLGCM implementation, gradient computation using Sobel or Prewitt operators followed by co-occurrence matrix generation through custom scripting is typically employed. After computation, these matrices can be utilized in various applications including image classification, pattern recognition, and image segmentation. Statistical features such as contrast, correlation, energy, and homogeneity can be derived from GLCM, while GLGCM combines texture and gradient characteristics for enhanced feature representation. In summary, GLCM and GLGCM serve as effective texture feature extraction methods applicable in image processing and computer vision domains. Their extraction methodologies can be efficiently implemented using MATLAB, providing crucial foundations for advanced image processing applications through programmable matrix computation and feature analysis routines.