# Multifilters-Based Unsupervised Method for Retinal Blood Vessel Segmentation
## 3 Materials and Methods
### Human Visual System Based Binarization
A comparison of our algorithm with the current state-of-the-art approaches is presented in Table 4 with respect to of Acc, Sen, and Spe on DRIVE and STARE benchmark datasets. \begin{table}
\begin{tabular}{c c c c c} \hline TP & a pixel decided by the proposed system as vessel pixel, and it represents also vessel pixel according to ground truth & & \\ \hline TN & a pixel decided by the proposed system as nonvessel pixel, and it is also non vessel pixel according to ground truth & & \\ \hline FP & a pixel decided by the proposed system as vessel pixel, but it is non vessel pixel according to ground truth & & \\ \hline FN & a pixel decided by the proposed system as non vessel pixel, but it represents vessel pixel according to ground truth & & \\ \hline \end{tabular}
\end{table} 
Table 1: Definitions of TP, TN, FP, and FN. \begin{table}
\begin{tabular}{c c c c c} \hline
**Scale \(k\)** & **Surround Size, \(S^{k}\)** & **Center Size, \(C^{k}\)** & \(W\)S\({}^{K}\) & \(W\)C\({}^{K}\)** \\ \hline \(k=\text{S}\), short & 7 & 0 & 33 & 25 \\ \hline \(k=\text{L}\), large & 9 & 3 & 90 & 75 \\ \hline \end{tabular}
\end{table} 
Table 2: Unoptimized values of HVS params. Figure 2: Best case accuracy: First column represents color images, second ground truth and third segmented images. First row contains DRIVE images, and STARE images are there in the second row. Last column: white indicate the pixels of true positives; while the green pixels denote the missed vessel pixels, whereas red are false positives. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**DRIVE**} & \multicolumn{4}{c}{**STARE**} \\ \hline
**Image No.** & **Sen** & **Spe** & **Acc** & **Sen** & **Spe** & **Acc** \\ \hline  
*Mean** & **0.7271** & **0.9798** & **0.9573** & **0.7164** & **0.9760** & **0.9560** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of suggested technique. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{3}{c}{**DRIVE**} & \multicolumn{3}{c}{**STARE**} \\ \hline
**Method/First Author** & **Year** & **Acc** & **Sen** & **Spe** & **Acc** & **Sen** & **Spe** \\ \hline \multicolumn{6}{c}{**Supervised**} \\ \hline [PERSON] [22] & 2018 & 0.9606 & 0.8014 & 0.9753 & 0.9435 & 0.8339 & 0.9536 \\ \hline [PERSON] [23] & 2019 & 0.9544 & 0.8175 & 0.9767 & 0.9656 & 0.8068 & 0.9838 \\ \hline [PERSON] [24] & 2020 & 0.9477 & 0.7338 & 0.9730 & 0.9498 & 0.7518 & 0.9734 \\ \hline [PERSON] [25] & 2020 & 0.9450 & 0.6994 & 0.9811 & 0.9486 & 0.6298 & 0.9839 \\ \hline [PERSON] [26] & 2021 & 0.958 & 0.786 & 0.973 & 0.953 & 0.831 & 0.9630 \\ \hline \multicolumn{6}{c}{**Deep Learning**} \\ \hline [PERSON] [27] & 2018 & 0.9542 & 0.7653 & 0.9818 & 0.9612 & 0.7581 & 0.9846 \\ \hline [PERSON] [28] & 2018 & 0.9480 & 0.739 & 0.956 & 0.947 & 0.748 & 0.9620 \\ \hline [PERSON] [29] & 2018 & 0.9624 & 0.7540 & 0.9825 & 0.9734 & 0.8352 & 0.9846 \\ \hline [PERSON] [30] & 2018 & 0.9556 & 0.7792 & 0.9813 & 0.9712 & 0.8298 & 0.9862 \\ \hline [PERSON] [31] & 2020 & 0.9649 & 0.8252 & 0.9787 & - & - & - \\ \hline [PERSON] [32] & 2020 & 0.9582 & 0.7996 & 0.9813 & 0.9672 & 0.7963 & 0.9863 \\ \hline [PERSON] [33] & 2021 & 0.9577 & 0.7918 & 0.9708 & 0.9445 & 0.8021 & 0.9561 \\ \hline \multicolumn{6}{c}{**Unsupervised**} \\ \hline [PERSON] [34] & 2018 & 0.9500 & 0.7100 & 0.9700 & 0.9500 & 0.7000 & 0.9700 \\ \hline [PERSON] [35] & 2019 & 0.9431 & 0.6129 & 0.9744 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of proposed method with other state-of-the-art methods. Figure 3: Worst case accuracy: First column indicate color images, second ground truth and third segmented images. First row contains DRIVE images and STARE images are there in the second row. Last column, white indicate the pixels of true positives; while green pixels denote the missed vessel pixels, whereas red refer false positives.