# Diabetic Macular Edema Characterization and Visualization Using Optical Coherence Tomography Images
## 3 Results
### Feature Selection  
To properly evaluate the meaning and significance of the feature selection, we have divided these results into two different points of view. These two points of view are presented in Figure 9. Figure 9a shows the percentage of occurrence of each category over the total of the Top 100 selected features by the SFS strategy. However, this metric is not able to fully illustrate the relevance of each class, since not all the feature categories that were considered have the same number of markers. For this reason, Figure 9b shows this same ranking, but weighted by the total number of available markers in each category. We can see how the HOG descriptor is heavily chosen for all the DME types. This detector, based on the analysis of the gradient orientations, is extremely useful in all the three cases: CME with orientations in all the directions, SRD with fusiform shapes and, on the other hand, DRT characterized by the direct absence of defined edges, presenting diffuse patterns (therefore, in the case of DRT, HOG acts as a negative discriminant, marking the absence of edges an indicative of the presence of DRT). On the other hand, we can observe that DRT also has less selected basic characteristics (GLDS) than for CME and SRD. This is due to the fact that DRT tends to be intermixed with healthy tissue and, specially in images that were taken with the Cirrus capture device, to present value ranges and distributions very similar to healthy tissue. Therefore, it is easier to distinguish DRT cases based on texture patterns rather than simply raw intensity data (again, specially with OCT images generated with the Cirrus OCT device). Figure 8: Steps to generate the binary maps. An overlap of 45 px was used between windows. This same fact has also influenced the total lack of selection of any of Gabor features. This descriptor, which as can be seen in Figure 8(a), stands out for the net amount of potential markers, being usually selected at the same ratio as HOG features since both offer good results when dealing with oriented gradients. However, Gabor is based on the decomposition of the image into its components in terms of the orientations and frequencies that make up the texture (similar to a Fourier transform). This strategy is not very useful in the case of DRT, since it does not present any regular texture pattern and, as we mentioned, the gray levels without spatial information are not very useful either when identifying this type of ME (which is why, as mentioned, the GLDS characteristics have not been selected either). This same case applies to the LAWS features, as they share similarities with Gabor and HOG descriptors. In the end, as the other aspects that Gabor and LAWS provided in addition to the information already stablished by the HOG features are not useful to detect the DRT type of ME, they were not selected in favor of simply using HOG. We can observe in Figure 8(b) the significant similarity between the characteristics that were selected for CME and SRD. This is due to the similarities in properties of both DME types, although with slight differences that manifest themselves in some of the selection proportions of various descriptors. For example, we see a higher rate of eigenvalues-based markers, which is appropriate due to the self-similarity present in the samples with SRD. There is also a lower rate of features from GLCM, since this type of fluid accumulation presents a simple pattern, always with hyperreflective to hyporeflective gradients as it is located around the retinal photoreceptor layer. Finally, we can also see a higher rate of selection of GLRLIS features compared to the CME ranking, which study the presence of strands of pixels with similar grey levels in different orientations. These are specially useful in this class because SRD-type fluid accumulations are usually either small in size or thin and elongated.