Statistical feature matrix for texture analysis

A new approach using the statistical feature matrix, which measures the statistical properties of pixel pairs at several distances, within an image, is proposed for texture analysis. The major properties of this approach are that (1) the size of the matrix is dependent on the maximum distance used instead of the number of gray-levels, (2) the matrix can be expanded easily and (3) some physical properties can be evaluated from the matrix. These properties have enhanced the practical applications of the matrix. In this paper, the matrix is applied to texture classification and visual-perceptual feature extraction. For texture classification, two experiments are performed. First, 16 Brodatz textures are employed to evaluate the performance of the matrix. A simple distance measure is defined to determine the similarity between two statistical feature matrices. Texture discrimination in an additive noise environment is also considered. Second, we apply the matrix to the classification of 150 sampled ultrasonic liver images. From experimental results it can be found that our approach is better than the spatial gray-level dependence method and the spatial frequency-based method. For visual-perceptual feature extraction, we evaluate five basic texture features, namely, coarseness, contrast, regularity, periodicity and roughness, from the statistical feature matrix. It is shown that the statistical feature matrix is an excellent tool for texture analysis.