## Materials and Methods
### Extraction of Texture Features
The LBP signature assigns a binary label, i.e., 0 or 1, to each neighbour depending on whether the central pixel \(z_{c}\) has higher intensity value than the neighbouring pixels. Such an LBP is not robust against rotations. The rotationally invariant LBP for \(z_{c}\), given a radius \(R\) and the number of involved neighbours \(P\), is given by:  
\[LBP_{ri}(z_{c})~{}=~{}\underset{0\leqslant n<P}{min}\Big{\{}\sum_{i=0}^{P-1}u (I(z_{i})-I(z_{c}))2^{(i+n)\%P}\Big{\}}, \tag{6}\]  
where \(z_{i}\) is the i-th neighbouring pixel, \(i~{}=~{}0,\ldots,P-1\). \(I(z_{i})\) is the grey level in pixels \(z_{i}\) and \(u(x)~{}=~{}1\) for \(x\geq 0\) and \(u(x)~{}=~{}0\). The symbol % denotes the remainder operation. The circular shift effect produced when the image is rotated is removed by finding the minimum value among all possible values. The original LBP texture descriptor yields a vector of \(2^{P}\) features, which is too large for higher values of \(P\) for many classifiers, especially if the number of input patterns is low. To overcome this drawback, we use the LBP uniform patterns, which refer to the patterns which have a limited number of transitions from 0 to 1 or vice versa, lower, or equal than two. In a circularly symmetric neighbour set of \(P\) pixels can occur \(P+1\) uniform binary patterns. In the uniform patterns, the number of 1's in the binary pattern is the label of the patterns, while the non-uniform patterns are labelled by \(P+1\). The histogram of the patterns' labels accumulated over the set \(S\) is employed as texture feature vector. So, we compute three texture feature vectors LBP\({}_{\text{Ri}}\) for each radius \(R_{i}~{}=~{}\{1,2,3\}\) using P = 8, and the feature vector LBP\({}_{\text{R123}}\) with 30 features for the concatenation of the previous three LBP vectors. The discrete wavelet transform (DWT) representation can be used as spectral texture descriptors. The DWT decomposes the image into low-pass and high-pass frequency bands. Applying recursively, the filters to the bands produce the wavelet decomposition. Therefore, the wavelet transform involves filtering and subsampling. A more compact representation is normally derived from the bands as texture descriptors. Some possible measures to compute over the transform coefficients for each sub-band and decomposition level are mean, energy, entropy, and standard deviation. The measures are computed using the pixels \((x,y)\in S\). We construct eight feature vectors DWT\({}_{\text{sfm}}\) varying the number of levels of decomposition \(s=\{2,3\}\), the number of filters \(f=\{LL,All\}\) (i.e., using only the low-pass sub-bands or using all sub-bands), and the number of measures \(m=\{EEMS,EMS\}\), i.e., using the four measures EEMS (energy, entropy, mean, and standard deviation) or EMS, excluding the entropy. Another popular family of texture features for classification is multi-scalar analysis, which encodes texture feature in different scales. This encoding can be done changing the size of neighbourhood in the statistical texture features, e.g., the LBP texture features or Haralick coefficients mentioned above or doing decompositions and down-sampling in the wavelet transforms, as the DWT\({}_{\text{sfm}}\) texture feature vectors. We also achieved multi-scalar analysis combining subsampling using wavelets with statistical analysis. Specifically, we compute the cooccurrence matrix and Haralick features over a wavelet decomposition in the following ways: (1) texture feature vectors Wavelet Co-occurrence Features, WCF\({}_{\text{dm}}\), and (2) texture feature vectors Wavelet Decomposition Cooccurrence Features, WDCF\({}_{\text{frm}}\). The features WCF\({}_{\text{dm}}\) are calculated applying the wavelet low-pass (L) and high-pass (H) filtering to the original image \(I\) to produce the sub-bands LL, LH, HL, and HH.