# 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors
## 5 Results and Discussion
\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 5: Classification results for the first experiment with the kNN classifier [%]. considered database when considering the SVM and RF techniques. Texture analysis methods based on handcrafted features do not require very large databases to perform well, as illustrated by the reported results on a relatively small dataset. However, this is not the case for deep-learning methods such as CNN. They need a large amount of training images to reach their maximum potential. This remark may contribute to the not top performance of the CNN method. Moreover, a more complex architecture or a larger number of epochs could improve the CNN accuracy, but of course, this would lead to increased processing times. The obtained classification scores show that the proposed feature extraction approach achieves the best performance for the considered database. The information gained by fusing the feature vectors of the BM3 DELBP_3D and IGLCM_3D increases the discrimination power, giving very high classification figures. Our method proves to be superior also with respect to the results obtained in [32]. The authors in [32] used the same dataset, the same image classes and the SVM classifier. They compared four feature extractors by adopting three different experimental setups. They achieved an accuracy ranging from 63% and 88% in case of the 2D single slice setup, between 66% and 91% for the 3D intra-slice configuration and between 74% and 86% for the 3D inter-slice approach. Figure 14 presents the confusion matrix obtained for one random partition of the training and test sets on the RFAI database by using the proposed feature extraction method and the SVM classifier. As shown in Figure 14, only one image from the test set was misclassified on this run: an image from class 4 (PerlinNoise) was predicted as being of class 8 (Uwari). The misclassified sample is a smoothed image. We show in Figure 15 the misclassified sample along with a smoothed training sample from the true class and a smoothed training sample from the predicted class. We can observe from Figure 15 that due to the applied Gaussian blur, the texture structures of the misclassified sample are altered. Visually, the sample in (**a**) is similar to the smoothed training samples of another class, Uwari. That is why, in this situation, a classification error appeared. In the second experiment, we performed the same tests, but we considered all images from the RFAI database. Tables 7-9 present the obtained classification results. Figure 14: The confusion matrix obtained by using the proposed method for a random partition.