# Multifilters-Based Unsupervised Method for Retinal Blood Vessel Segmentation
## 3 Materials and Methods
### Human Visual System Based Binarization
In Table 4, it can be observed that the suggested technique has demonstrated a similar performance as state-of-the-art approaches including supervised or unsupervised algorithms with respect to accuracy. Furthermore, as expected, most of the deep learning-based approaches performed better than the proposed method. In the unsupervised category on the DRIVE dataset, the proposed algorithm realizes an accuracy of 0.9573, which is the best amongst all the methods described under the unsupervised category in Table 4. Similarly, we achieved an accuracy of 0.9560 on the STARE dataset, which is the second highest (the highest being the method of [PERSON] et al. [38]). Additionally, among the unsupervised category, the proposed system achieves the second highest specificity after [PERSON] et al. 's method [41]. However, with the sensitivity of 0.7 or more the proposed system has the highest specificity among all the methods described under supervised and unsupervised categories on the DRIVE dataset, as shown in Table 4. To summarize, deep learning methods have performed much better with regard to Sen, Spe and Acc as compared to conventional methods both supervised and unsupervised. The deep learning method given in [29] reports the best results for both the DRIVE and STARE datasets. These are 0.9624, 0.7540 and 0.9825 for DRIVE and 0.973, 0.8352 and 0.9846 for STARE respectively, for Acc, Sen and Spe. However, these high values are obtained at a cost of the high computational requirement by a deep learning algorithm. Similarly, in conventional methods (other than deep learning), supervised approaches realize better performance than unsupervised approaches. For example, the best results in terms of Acc, Sen and Spe, reported in [22], are 0.9606, 0.8014 and 0.9753, respectively, on the DRIVE dataset. While, on the STARE dataset, 0.9656, 0.8068 and 0.9838 are obtained for Acc, Sen and Spe, respectively, reported in [23]. On the other side, supervised methods require intense training which necessitates computational resources and time. Table 5 presents the execution time comparison. It is not possible to exactly judge the performance of the systems based on execution time as the systems have different hardware specification and software used. But it gives some clue about the execution time requirements. It is vivid from the Table 5 that the proposed system is comparatively better than many of the entries in the table based on execution time and accuracy. The diseases such as hypertension and diabetes can be diagnosed at early stage, through the identification of variations in the retinal blood vessels. The accurate segmentation of blood vessels helps to provide such information to ophthalmologists for better disease characterization [43]. Our method has shown promising results for large to medium size blood vessel segmentation similar to state-of-the-art methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Dataset** & \multicolumn{3}{c}{**DRIVE**} & \multicolumn{3}{c}{**STARE**} \\ \hline
**Method/First Author** & **Year** & **Acc** & **Sen** & **Spe** & **Acc** & **Sen** & **Spe** \\ \hline [PERSON] [36] & 2019 & 0.9300 & 0.6900 & 0.9400 & - & - & - \\ \hline [PERSON] [37] & 2019 & 0.9553 & 0.8043 & 0.9730 & 0.9545 & 0.8011 & 0.9694 \\ \hline [PERSON] [38] & 2020 & 0.9560 & 0.7890 & 0.9720 & 0.9610 & 0.7360 & 0.9810 \\ \hline [PERSON] [39] & 2020 & 0.9480 & 0.7375 & 0.9788 & 0.9542 & 0.7484 & 0.9780 \\ \hline [PERSON] [40] & 2020 & 0.9552 & 0.7738 & 0.9721 & 0.9543 & 0.7769 & 0.9688 \\ \hline [PERSON] [41] & 2021 & 0.9554 & 0.6942 & 0.9802 & 0.9492 & 0.7019 & 0.9771 \\ \hline [PERSON] [42] & 2021 & 0.9519 & 0.7667 & 0.9692 & 0.9524 & 0.7969 & 0.9664 \\ \hline
**Proposed Method** & **2022** & **0.9573** & **0.7271** & **0.9798** & **0.9560** & **0.7164** & **0.9760** \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._