Image Texture Segmentation Using Gray-Level Co-occurrence Matrix Method

In the field of digital image processing today, the gray-level co-occurrence matrix (GLCM) method has become a widely adopted technique for image texture segmentation. This approach characterizes texture features by calculating the frequency and spatial relationships of pixel intensity pairs within an image. The GLCM-based methodology not only enables texture segmentation but also finds applications in image classification, object recognition, and various other domains. For individuals seeking deeper understanding of digital image processing techniques, learning and mastering the GLCM method proves highly essential and beneficial.

Implementation typically involves four key steps: computing GLCM for different orientations (0°, 45°, 90°, 135°) using functions like graycomatrix(), extracting texture features (contrast, correlation, energy, homogeneity) via graycoprops(), applying feature selection algorithms, and performing segmentation using clustering methods (K-means) or thresholding techniques. The MATLAB implementation commonly utilizes the Image Processing Toolbox functions where graycomatrix() generates the co-occurrence matrix with specified parameters (distance, orientation), while graycoprops() quantifies texture characteristics from the computed matrix.