# 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors
## 5 Results and Discussion  
We compared the results obtained using the proposed method with the approaches in [8; 11]. In the first experiment, we considered nine image classes built using an interpolation approach. Tables 4-6 show the obtained average accuracy and macro-averaging  
Figure 13: Examples of images constructed by: (**a**) using the Fourier Transform (**b**) considering a random distribution of geometric shapes (**c,d**) using a combination of all these methods including interpolation. precision for each considered approach for the first experiment. The macro-averaging recall is identical to the average accuracy for the considered database (because the number of samples is the same for each category). For each image class, 75% of the image samples were used for training and 25% for testing. All parameters used in the experimental configuration are given in Appendix A. From the obtained results, we can see that the SVM classifier achieved the best performance for all considered methods. The results show that IGLCM_3D surpasses the 3D GLCM. This proves that the additional information provided by the gradient image is discriminative, being able to increase the classification performance. The proposed indicators based on the gradient magnitude and the gradient orientation are able to offer important information regarding the distribution of strong and weak edges and the local anisotropy in the considered textures. The 2D version of LBP achieves a poor classification performance. This happens because it does not take into consideration the relations between different slices in a volumetric image as the BM3 DELBP_3D does. The BM3 DELBP_3D operator surpasses the GLCM-based methods and the 2D LBP since it offers a good robustness to noise and an increased discrimination power. In [8], the 2D LBP method and their proposed 3D version of LBP achieve a similar performance on noisy and normal images from the same database. The CNN approach achieves a good performance. However, the feature vector provided by BM3 DELBP_3D proves to be better in terms of discrimination power for the \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Average Accuracy and** & **Macro-Averaging Precision** \\
**Metric/Operator** & **Associated Standard** & **and Associated Standard** \\  & **Deviation** & **Deviation** \\ \hline
**3D GLCM [11]** & 70.53 \(\pm\) 4.63 & 71.83 \(\pm\) 5.12 \\
**IGLCM_3D [29]** & 79.93 \(\pm\) 4.27 & 80.97 \(\pm\) 4.46 \\
**LBP_2D [8]** & 69.36 \(\pm\) 8.41 & 74.73 \(\pm\) 7.27 \\
**BM3 DELBP_3D [28]** & 87.44 \(\pm\) 3.35 & 88.33 \(\pm\) 3.38 \\
**Proposed method** & **96.44 \(\pm\) 1.96** & **96.72 \(\pm\) 1.88** \\ \hline \hline \end{tabular} The results are averaged over 100 random partitions of the train and test sets.  
\end{table} 
Table 6: Classification results for the first experiment with the RF classifier [%]. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Average Accuracy and** & **Macro-Averaging Precision** \\
**Metric/Operator** & **Associated Standard** & **and Associated Standard** \\  & **Deviation** & **Deviation** \\ \hline
**3D GLCM [11]** & 74.01 \(\pm\) 4.36 & 75.27 \(\pm\) 4.5 \\
**IGLCM_3D [29]** & 86.44 \(\pm\) 3.09 & 87.4 \(\pm\) 3.24 \\
**LBP_2D [8]** & 78.02 \(\pm\) 7.54 & 81.1 \(\pm\) 6.6 \\
**BM3 DELBP_3D [28]** & 94.2 \(\pm\) 2.66 & 94.76 \(\pm\) 2.32 \\
**Typical CNN** & 92.88 \(\pm\) 3.1 & 93 \(\pm\) 1.72 \\
**Proposed method** & **99.63 \(\pm\) 0.63** & **99.67 \(\pm\) 0.56** \\ \hline \hline \end{tabular} The results are averaged over 100 random partitions of the train and test sets.  
\end{table} 
Table 4: Classification results for the first experiment with the SVM classifier [%].