Implementation of Gray-Level Co-occurrence Matrix and Its Features Using MATLAB

In this article, we will discuss how to implement gray-level co-occurrence matrix (GLCM) and extract its statistical features using MATLAB. GLCM is a widely-used method for image feature extraction that characterizes the spatial relationships between pixels in an image. By computing the GLCM and its statistical features, we can obtain valuable information about image texture and structure, which is essential for various image processing applications such as pattern recognition, medical imaging, and remote sensing analysis. Before implementing the GLCM and its features, it's crucial to understand the fundamental concepts and computational methods. The GLCM is constructed by counting the frequency of pixel pairs with specific gray-level values occurring at defined spatial relationships. It effectively captures the spatial distribution of different gray levels within an image. Based on the relative positions and gray-level differences between pixel pairs, GLCM can be calculated for different angles (typically 0°, 45°, 90°, and 135°) and distances. Each GLCM yields four primary statistical features: energy (angular second moment), contrast, correlation, and homogeneity (inverse difference moment), which respectively measure uniformity, local variations, linear dependencies, and local homogeneity of the image texture. For practical implementation, MATLAB provides specialized functions through its Image Processing Toolbox. The graycomatrix function calculates the GLCM by accepting parameters such as the number of gray levels, spatial relationships (distance and angle), and normalization options. Key implementation considerations include specifying the GrayLimits parameter to define the intensity range and using the NumLevels parameter to control quantization. Following GLCM computation, the graycoprops function extracts the statistical features by analyzing the matrix properties. This function supports feature selection through its properties parameter, allowing users to compute specific texture measures relevant to their application. The implementation typically involves preprocessing the input image, computing the GLCM with appropriate parameters, and then extracting the desired features for further analysis or classification tasks.