MATLAB Implementation of Gray-Level Co-occurrence Matrix

The MATLAB implementation of Gray-Level Co-occurrence Matrix (GLCM) utilizes the joint probability matrix method to statistically analyze all pixels in an image, providing a comprehensive description of its gray-level distribution. The method constructs a matrix by calculating the frequency of occurrence between pairs of pixels with specific gray-level values at defined spatial relationships (typically using offset parameters like [1,0] for horizontal neighbors). This matrix effectively captures the correlation between different gray-level intensities, revealing texture patterns in the image. Key MATLAB functions involved in this implementation include graycomatrix() for matrix generation and graycoprops() for feature extraction. The GLCM calculation typically involves specifying parameters such as the number of gray levels (through quantization), offset directions, and distance between pixels. From the resulting matrix, important texture features like contrast, correlation, energy, and homogeneity can be derived using mathematical operations on the matrix elements. These extracted features serve as valuable descriptors for various computer vision tasks including image classification, pattern recognition, and texture analysis. The MATLAB implementation of GLCM thus provides researchers and developers with a powerful tool for advanced image processing and computer vision applications, enabling quantitative analysis of texture characteristics through programmable, customizable parameter settings.