# Diabetic Macular Edema Characterization and Visualization Using Optical Coherence Tomography Images
## 2 Materials and Methods
### ME Classification
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Indexes** & **Description** \\ \hline Basic Gray-Level Distribution Statistics (GLDS) & 1–15 & \begin{tabular}{c} Descriptors of the statistical gray-level distribution \\ by prevalence, not spatial distribution \\ (like the max., min., mean, median...) \\ \end{tabular} \\ \hline Eigenvectors & 16–43 & \begin{tabular}{c} Study on the principal and last eigenvectors of \\ the sample, as well as different relations between them. \\ \end{tabular} \\ \hline Histogram Analysis & 44–48 & \begin{tabular}{c} Additional statistical analysis descriptors \\ describing the shape of the histogram \\ (like the kurtosis, obliquity, energy...) \\ \end{tabular} \\ \hline Gray Level Co-ocurrence Matrix (GLCM) & 49–64 & \begin{tabular}{c} Texture descriptor focused on describing the \\ probability of two different gray levels being \\ together at a certain angle and distance. \\ \end{tabular} \\ \hline Histogram of Oriented Gradients (HOG) [38] & 65–145 & \begin{tabular}{c} Edge-based feature, describing the texture in \\ terms of the orientations of its gradients. \\ \end{tabular} \\ \hline Gabor filters [39] & 146–273 & \begin{tabular}{c} Decomposition of the sample in their \\ components, analog to the Fourier transform. \\ \end{tabular} \\ \hline Local Binary Patterns [40] & 274–337 & \begin{tabular}{c} Texture descriptor resilient to intensity \\ variations, thanks to expressing the texture in terms of \\ relative intensity in the neighborhood. \\ \end{tabular} \\ \hline LAWS filters [41] & 338–365 & \begin{tabular}{c} Convolution masks describing texture \\ based on different pre-stablished patterns \\ like edges, ripples and dots. \\ \end{tabular} \\ \hline Fractal Analysis [42] & 366–368 & \
\begin{tabular}{c} Features derived from evaluating the fractal properties \\ of the texture, describing its self-similarity and lacunarity. \\ \end{tabular} \\ \hline Gray-Level Run Length Image & 369–375 &
\begin{tabular}{c} Descriptor based on evaluating the \\ length, in different orientations, of pixel \\ strips with similar gray levels. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the extracted feature types. the positive and negative labels. To achieve this, for each type of pathology, \(N/3\) samples were chosen at random from each of the datasets that provided the samples for the negative class, \(N\) being the number of samples of the positive class. For example, when generating an hypothetical CME dataset (thus CME would be the positive class), if the CME dataset had 100 samples, 33 samples would be taken from the DRT, SRD and dataset with samples not belonging to fluid regions. For the training, we have chosen four models that represent different paradigms tested in the domain, which have also obtained positive results in previous experiments related to the identification of intraretinal fluid. Thus, the paradigms we used are a k-Nearest Neighbors (kNN) with k = 15, a Linear Discriminant Classifier (LDC), a Random Forest (RF) and a Support Vector Machine (SVM) with a Radial Basis Function (RBF) or Gaussian as kernel. Finally, to select the optimal number of features to train the final models, we analyzed the results that were obtained by fitting a degree four polynomial curve to the point cloud corresponding to the cross validations made with incremental feature subsets. By doing so, we analyzed the trend of the results from a global behaviour point of view instead of only each independent subset of characteristics (prone to the inherent variability of the designed training strategy). Finally, we do not keep the model that obtained the best result in the adjusted curve (since it may have simply obtained slightly better results than a previous subset by chance or at the cost of generalisation capabilities because it was significantly more complex). Instead, we study the shape of the fitted curve, keeping the simplest classifier (i.e., trained with fewer characteristics) that has reached a positive peak.