Texture Feature Extraction Based on Gray-Level Co-occurrence Matrix Using MATLAB

MATLAB-based texture feature extraction is a widely used technique in the field of image processing that utilizes gray-level co-occurrence matrix (GLCM) to characterize texture patterns. This method enables comprehensive analysis of texture information within images and plays a significant role in applications such as image classification, recognition, and segmentation. In MATLAB implementation, the graycomatrix() function calculates the GLCM by analyzing spatial relationships between pixel pairs with specific distance and angle parameters, while graycoprops() extracts statistical features like contrast, correlation, energy, and homogeneity from the generated matrix. While alternative methods exist for texture feature extraction including wavelet transforms, local binary patterns (LBP), and histogram of oriented gradients (HOG), the GLCM-based approach remains one of the most prevalent techniques due to its straightforward implementation and computational efficiency. The MATLAB environment provides optimized functions for GLCM calculation and feature extraction, typically requiring only 3-5 lines of core code for basic implementation. For example, a standard workflow involves converting images to grayscale, computing GLCM with specified parameters, and extracting statistical features using built-in functions. Through MATLAB-based texture feature extraction methods, researchers and engineers can effectively quantify texture characteristics and leverage this information for various image processing applications. The method's robustness makes it particularly suitable for texture analysis in medical imaging, remote sensing, and industrial inspection systems.