Table 3.

The summary of the statistical model used in texture quantification

	Statistical Model
	First-order	Second-order	Higher-order
Meaning	Frequency distribution of pixel/voxel gray-values without considering their spatial orientation [45].	Spatial distribution of pixel/voxel gray-levels in relation to their relative positions [46]	Characterizing images based on a unique interaction between the pixels/voxels that constitute the image [25].
Computation method	Histogram from which several texture features can be derived	Texture features obtained from the joint probability distribution of neighboring pixels	Mathematical algorithms that evaluate pixel intensities in relation to their neighboring pixels
Examples	mean gray-level intensity, uniformity, entropy, standard deviation, skewness, kurtosis	GLCM, GLRLM	NGTDM, NSZM, wavelet, and Gabor transform

GLCM gray-level co-occurrence matrix, GLRLM gray-level run-length matrix, NGTDM neighborhood gray-tone difference matrix, NSZM neighborhood size zone matrix