**4. Extraction of color texture features**

Color is one of the better notable and striking visual aspects that is employed in image retrieval and pattern recognition. Color moment (CM) is a computation technique utilized to discriminate images based on their color distribution in the image similar to the central tendency of the probability distribution. It is a potential technique for the description of color features [26, 27]. Once determined, these moments contribute a quantity for color resemblance among images. The red plane, green plane and blue plane images are extracted from each of the segmented thermal images for the control and DM groups. In this study, the mean (first moment), standard deviation (second moment), skewness (third moment), kurtosis (fourth moment), variance and entropy are extracted for all the four ROIs in each foot of all three color planes of images. The mean represents the average color value existing in the image. Variance is a measure of the color distribution of the image. Standard deviation is attained by executing a square root of the variance of the color distribution. Skewness is a measure of the degree of symmetry distribution of the color. The color textural features extracted from the green plane images of the left and right foot are highly correlated and shows very few dissimilarities within the values for all the four ROIs. Hence, the color texture features extracted from red and blue plane images alone are used for further processing. The skewness is having negative values for a few ROIs is the representation of the tail at the smaller end of the textural distribution is more pronounced than the tail at the larger end of the textural distribution.
