## 3 Results
### Segmentation Tasks
#### 3.1.6 Machine Learning
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task** & **Method** & **First** & **Database** & **Description** & **Results** \\  & **Author (Year)** & **Field of View (FOV)** & & & \\ \hline \multirow{9}{*}{**Task**} & [PERSON] 2016 [39] & 5 subjects & Global threshold to remove FAZ, Hessian filter, **local mean** & No segmentation validation. \\  & [PERSON] 2016 [39] & 6.72 \(\times\) 6.27 mm\({}^{2}\) & **adaptive threshold**, skeletonization. & Reproability and usefulness of parameters. \\ \cline{2-5}  & [PERSON] 2016 [40] & 84 DR, 14 healthy & Global threshold to remove FAZ, Hessian filter, **local median** & No segmentation validation. \\  & [PERSON] 2016 [40] & 2D & **adaptive threshold**—top hat filter and combination of & Negative correlation between DR severity and SVD, VD, PC9 positive \\  & [PERSON] & 3 \(\times\) 3 mm\({}^{2}\) & binarized images. & correlation with VD1 \\ \cline{2-5}  & [PERSON] 2017 [25] & 36 SCR patients, 26 healthy & **Global thresholding**, morphological functions, and fractal dimension analysis. & No segmentation validation. \\  & [PERSON] 2017 [25] & 3 \(\times\) 3 mm\({}^{2}\) & **dimension analysis**. & 4. Vascular density was more sensitive to SCR presence than vessel tortuosity and mean diameter. \\ \cline{2-5}  & Omg 2017 [29] & 38 glaucoma, 120 non glaucoma & **Global thresholding**, morphological dilation, dosing. & No segmentation validation. \\  & & 6 \(\times\) 6 mm\({}^{2}\) & **Method proposed for classification.** \\ \cline{2-5}  & [PERSON] 2019 [21] & 20 DR, 6 AMD, 1700, 26 healthy & **Frang filter, Otsu thresholding.** & No segmentation validation. \\  & Alharoyo 2019 [20] & 3 \(\times\) 3 mm\({}^{2}\) & **orientation.** & Method proposed for classification. \\ \cline{2-5}  & Al Alam 2019a [30] & 100 images/50 subjects & bias field correction, matched filtering method, bottom hat filtering & No segmentation validation. \\  & Alam 2019a [30] & 2D & **global thresholding + adaptive thresholding**, morphological operations. & Method proposed for classification. \\  & Alam 2019b [42] & 60 DR, 90.50 CPU 40 healthy & **Frang filter, adaptive thresholding with morphological functions,** & No segmentation validation. \\  & Alam 2019b [42] & 6 \(\times\) 6 mm\({}^{2}\) & ** skeletonization.** & Method proposed for classification. \\ \cline{2-5}  & Pappelis 2019 [31] & 30 healthy & **Local Ols thresholding for all vessels, big blood vessels masked** & No segmentation validation. \\  & & 6 \(\times\) 6 mm\({}^{2}\) & out through Frang and **global thresholding**. & Reproability of vessel density and flux. \\ \hline \multirow{2}{*}{**Xa 2019 [22]**} & 123 DR, 10 healthy & Multi-scale line detector, **Otsu thresholding for large vessel segmentation**. & No segmentation validation. \\  & Alam 2019 [22] & 6 \(\times\) 6 mm\({}^{2}\) & vessel segmentation, skeletonization. & Reproability and differences between healthy and diseased. \\  & Alam 2020 & 30 DR, 30 DR, 40 healthy & Contrast and resolution enhancement, Frang filter, **global thresholding**. & No segmentation validation. \\  & Alam 2019 [32] & 3 \(\times\) 3 mm\({}^{2}\) & **thresholding.** & Method proposed for classification. \\ \hline \multirow{2}{*}{**Adradade De Jesus** 2020 [24]**} & 82 glaucoma, 39 healthy & **3880** percentile of image intensity histogram**, morphological & \\  & Alam 2019 [24] & 3 \(\times\) 3 mm\({}^{2}\) & **opening and closing, small object removal.** & No segmentation validation. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Segmentation tasks summary. \begin{table}
\begin{tabular}{c c c c} \hline \hline