Table 1. Feature extraction algorithms and lists of features derived.

Description	Calculation formula	Feature derived
Gray-level histogram
Mean measures the average value of the histogram		his_mean_σ
SD measures the stability of the gray level histogram		his_SD_σ
Percentile mean and SD measures are calculated from the top 50%, 25%, and 10% of the histogram curve		his_β_mean_σ
		his_SD_β_σ
Kurtosis describes the sharpness of the histogram		kurtosis_σ
Skewness describes the degree of asymmetry around the mean value in the gray level histogram		skewness_σ
Gray-Level Co-Occurrence Matrix (GLCM)
Contrast measures local intensity variation, reflects the uniformity of image grayscale distribution and the degree of thickness in texture		contrast_α_σ
Correlation measures the gray level linear dependence between the pixels at the specified positions relative to each other		correlation_α_σ
Entropy describes the inhomogeneity of an image		entropy_α_σ
Energy is the sum of squares of entries in the GLCM, measures the image homogeneity		energy_α_σ
Homogeneity weights as the inverse of the Contrast weight		homogeneity_α_σ

Note: X(i) indicates the intensity of gray level i; N denotes the sum of pixels in the image; β indicates the top percentage of the histogram curve, which could be 50%, 25%, and 10%; M denotes the number of pixels in the histogram on the percentage of (1 − β); x, y denote the spatial coordinates of the pixel; P(i, j) is the co-occurrence matrix by the δ = 1 and θ(0°, 45°, 90°, 135°); N_g denotes the number of discrete intensity levels in the image; μ is the mean of P(i, j); μ_x(i) is the mean of P_x(i); μ_y(j) is the mean of P_y(j); σ_x(i) is the standard deviation of P_x(i); σ_y(j) is the standard deviation of P_y(j). σ represents the filter value applied, which could be 0, 1.0, 1.5, 2.0 and 2.5. α represents the considered direction, which could be 0°, 45°, 90°, and 135°. β represents the top percentage of the histogram curve, which could be 50%, 25%, and 10%.