Calculation of Gray-Level Co-occurrence Matrix (GLCM)

The Gray-Level Co-occurrence Matrix (GLCM) is a widely used statistical method in image texture analysis that characterizes texture features by calculating the joint probability distribution of pixel pairs in an image. This matrix captures comprehensive information about image grayscale properties including direction, spatial relationships, and variation magnitude.

The GLCM calculation process primarily involves the following steps: 1. Define the direction and distance for pixel pairs. Common directions include 0°, 45°, 90°, and 135°, with the distance typically set to 1 pixel unit. 2. Count the frequency of pixel pairs with specific gray-level values occurring in the specified direction. 3. Normalize the matrix to convert frequencies into probability values. In code implementation, this typically involves creating a nested loop to traverse pixel neighborhoods, using conditional statements to verify directional relationships, and applying matrix normalization functions.

In practical applications, GLCM is not used directly but rather serves as the basis for extracting secondary statistical features. These features include: - Contrast: Reflects image clarity and depth of texture grooves (implemented through weighted sum of squared differences) - Energy: Represents the uniformity of gray-level distribution (calculated as the sum of squared matrix elements) - Entropy: Indicates the complexity of image texture (computed using probability-based logarithmic operations) - Correlation: Measures the degree of gray-level linear relationships (derived from mean and standard deviation calculations)

The advantage of GLCM lies in its effective characterization of texture coarseness and directionality, making it suitable for various texture analysis scenarios. However, it's important to note that GLCM computation can be computationally intensive, particularly for high-resolution images. Preprocessing techniques such as image downsampling are often necessary to improve computational efficiency. Code optimization strategies may include parallel processing for pixel pair counting and matrix operations using vectorized computations.