# 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors
## Appendix A Parameters for the Methods Considered in the Experimental Section  
A Z-score normalization is performed before classification for all handcrafted feature vectors. The SVM parameters are obtained through a grid search to achieve the best classification accuracy. The only parameter needed to be tuned for the kNN technique is the number of neighbours which was chosen to be 5 by considering a trial and error approach. For the RF classifier, we considered 90 trees. As suggested in [33], a range between 64 and 128 ensures an optimal trade-off between accuracy and processing time. The extension of the BM3 DELBP to 3D  
This algorithm uses the radii \(\mathrm{R}~{}=~{}2\) and \(\mathrm{R}~{}=~{}4\) chosen as a trade-off between discrimination power and feature vector size. Additionally, the number of neighbours is \(\mathrm{P}~{}=~{}8\) for both radii, chosen to minimize the size of the feature vector and implicitly the computation complexity. The noise standard deviation for the BM3D filter [24] is \(\mathrm{m}=18\) (this parameter is tuned to obtain the highest classification accuracy) for the first experiment and \(\mathrm{m}=17\) for the second experiment. The size of the final feature vector is \(\mathrm{2~{}scales}~{}\times~{}3\) planes \(\times\) (nr_of_bins_Cl + nr_of_bins_NI + nr_of_bins_RD) \(=2\times 3\times\) (\(2+9+9\)) \(=~{}120\) values. The SVM parameters [34] used are: RBF (Radial Basis Function) kernel, C = 100 and \(\gamma\) = 0.001 for the first experiment and C = 100 and \(\gamma\) = 0.01 for the second experiment. 3D GLCM  
There are used 13 directions and 2 distances: \(\mathrm{d}~{}=~{}1\) voxel and \(\mathrm{d}~{}=~{}2\) voxels. The size of the final feature vector is: 14 Haralick indicators \(\times~{}2\) distances \(\times~{}1\) mean value \(=~{}28\)values and the SVM parameters [34] used are: RBF kernel, C = 1000, \(\gamma\) = 0.0125 for the first experiment and C = 1000, \(\gamma\) = 0.01 for the second experiment. For providing a degree of rotation invariance, we consider the average (mean value) of the Haralick features for the 13 directions. The 2 feature vectors obtained for the 2 distances are concatenated. 3. IGLCM_3D Similar parameters as for the 3D GLCM are used. The Haralick feature vector is of size 56: 14 Haralick indicators \(\times\) 2 distances (d = 1 and d = 2) \(\times\) 2 statistical measures (mean and standard deviation). The gradient magnitude-based feature vector has 16 values: 4 gradient based indicators \(\times\) 2 distances (d = 1 and d = 2) \(\times\) 2 statistical measures (mean and standard deviation), while the azimuth and orientation-based feature vectors contain 4 values each: 1 orientation-based indicator \(\times\) 2 distances (d = 1 and d = 2) \(\times\) 2 statistical measures (mean and standard deviation). The size of the final feature vector is, therefore: 56 + 16 + 8 = 80 values. The considered SVM parameters [34] are: RBF kernel, C = 1000, \(\gamma\) = 0.0125 for the first experiment and C = 100, \(\gamma\) = 0.1 for the second experiment. We consider the average and standard deviation of all indicators for the 13 directions; the final feature vector concatenates the obtained characteristics for the 2 considered distances. 4. LBP_2D This algorithm is the uniform rotation invariant LBP with unitary radius and 8 neighbours. The number of bins computed for a histogram for a single slice is 9 (1 is non-uniform and is not taken into consideration). The size of the final feature vector is: 9 bins \(\times\) 64 slices \(=\) 576 values. The SVM parameters [34] used are: RBF kernel, C = 100 and \(\gamma\) = 0.001 for the first experiment and C = 100 and \(\gamma\) = 0.01 for the second experiment. 5. CNN approach We considered a typical CNN approach having the architecture given in Table 1. The input layer performs a Z-score normalization, the considered solver is the stochastic gradient descent with momentum, the learning rate is 0.01, the mini-batch size is 15 and the number of epochs is 15 for the first experiment and 30 for the second one. 6.