Gray level co-occurrence matrix in polar orientation

Classification of gray images based on their textural features is one of the main tools in medical
image processing. Gray Level Co-occurrence Matrix (GLCM) is such a widely used technique
which represents how frequently the different gray level combinations occur in an image,
traditionally in Cartesian directions. Contrast, correlation, energy and homogeneity are features
based on the calculated GLCM. However, in human anatomy, structures often take a curvilinear
pattern and therefore the Cartesian GLCM may not be very efficient in medical imaging. In this
study, an algorithm was developed to calculate the GLCM in radial and circumferential
directions. The texture parameters calculated using the polar GLCM were then tested against
those calculated using the traditional Cartesian GLCM, by means of simulated images with
varying speckle features. Our results show that the Polar GLCM is better at detecting changes
in number of speckles in radially and circumferentially oriented speckled images than the
Cartesian GLCM even in the presence of noise.