. Author manuscript; available in PMC: 2020 Jun 11.

Published in final edited form as: Proc SPIE Int Soc Opt Eng. 2019 Mar 8;10951:109512I. doi: 10.1117/12.2512282

Table I.

List of tested texture features and their descriptions.

Feature	# of features	Description
First-order features	10	Intensity of the voxel at the patch center, mean (μ_p), median (m_P), standard deviation (σ_p), minimum, maximum, skewness [23], kurtosis [23], energy [23], and entropy [23] of a 3D patch intensity histogram. The first-order texture features are measured based on the histogram of the image, therefore the 2D implementations could be easily generalized for 3D input patches.
Histogram of oriented gradients (HOG)²⁴	8	The distribution of intensity gradients. In this study, we formed HOG as a histogram of eight, 2D orientation bins. To obtain HOG for 3D patches, we calculated one histogram per 2D axial slice and measured the average of all the corresponding HOG bins to form one, eight-bin HOG.
Histogram of Local binary patterns (LBP)²⁵	8	We calculated LBP in 3D within a 3×3×3 mask and formed one, eight-bin histogram per image patch.
Grey-level co-occurrence, matrices- based²⁶ (GLCM)-based features	32	We defined GLCM for a 3D patch as the average of the GLCMs of all the 2D axial patch slices related to the same 2D pixel neighboring. In this study we measured GLCM-based features based on four 2D offsets, i.e. (−1,0), (0,−1), (1,−1), (−1,−1). The features consist of entropy, energy, contrast [23], homogeneity [27], inverse different moment [23], correlation [23], cluster shade [27], and cluster prominence [27] of the four GLCMs.
Mean gradient angle	1	For a 3D patch, we measure mean gradient angle as the average of mean gradient angles of all the 2D axial slices.
Edge-based features	8	We formed an eight-bin histogram of edge directions for a 3D patch. The edges are detected using Sobel operator [28] as an edge detector for all the 2D slices.
Total	67