# Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images
## 4 Experiment
### Parameters
#### 4.4.2 Discussion  
In the DRISHTI-GS and TMUEH datasets, the average IoU achieved 0.9257 and 0.8179, and the average FSE achieved 4.6436 and 3.5946, respectively. In the DRISHTI-GS dataset, 99 in 101 cases (98%) had an IoU value of more than 0.8, and 83 in 101 cases (82%) had 5 points with the FSE evaluation method. In the TMUEH dataset, 28 in 37 cases (76%) had an IoU value of more than 0.8, and 29 in 37 cases (78%) had 3 or more points with the FSE evaluation method. As shown in Figures 23 and 24, there was no depression or there was only a little depression in the blood-vessel area. This proves that the morphological-processing-based blood-vessel removal and frequency-domain-based noise removal were effective. In Figures 23b and 24b, there was bright noise surrounding the OD in these cases due to the constraint of the shape-based term on the contour evolution. The HLSM avoided these effects and achieved outstanding segmentation results. In Figures 23c and 24c, the PPA regions were also ignored in these cases. This demonstrates that the adaptive-thresholding-based ellipse fitting was able to detect a more suitable initial value. Furthermore, under the constraint of the shape-based term, the effect of the PPA regions was avoided. These segmentation results prove that the problems of occlusion by blood vessels, and the effects by bright noise and PPA regions were solved with the proposed multiple preprocessing HLSM. As shown in Table 6, the proposed multiple preprocessing HLSM achieved better accuracy than that of other recent algorithms. As shown in Tables 7 and 8, the proposed \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Active** & **Threshold-** & \multirow{2}{*}{**Proposed**} \\  & **Contour-Based**[31] & & & \\ \hline Average FSE & 3.4595 & 3.5135 & **3.5946** \\ FSE = 5 & 12/37 cases & 14/37 cases & 12/37 cases \\ FSE = 4 & 11/37 cases & 6 /37 cases & 10/37 cases \\ FSE = 3 & 3/37 cases & 7/37 cases & 7/37 cases \\ FSE = 2 & 7/37 cases & 7/37 cases & 5/37 cases \\ FSE = 1 & 1 case/37 cases & 1 case/37 cases & 2/37 cases \\ FSE = 0 & 3/37 cases & 2/37 cases & **1 case/37 cases** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of the results in the TMUEH dataset with the reproduced algorithms by FSE. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Active** & **Threshold-** & \multirow{2}{*}{**Proposed**} \\  & **Contour-Based**[31] & & & \\ \hline Maximal IoU & 0.9425 & 0.9419 & 0.9300 \\ Minimal IoU & 0.1711 & 0.4671 & **0.5205** \\ Average IoU & 0.7321 & 0.7614 & **0.8179** \\ Variance in IoU & 0.0330 & 0.0141 & **0.0104** \\
0.9 \(\leq\) IoU & 3/37 cases & 4 /37 cases & **8/37 cases** \\
0.8 \(\leq\) IoU\(<\) 0.9 & 16/37 cases & 15/37 cases & 20/37 cases \\ IoU\(<\) 0.8 & 18/37 cases & 18/37 cases & **9/37 cases** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of results in the TMUEH dataset with reproduced algorithms with IoU. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Active** & **Threshold-** & \multirow{2}{*}{**Proposed**} \\  & **Contour-Based**[31] & & & \\ \hline Average FSE & 4.3069 & 4.3762 & **4.6436** \\ FSE = 5 & 60/101 cases & 74/101 cases & **83/101 cases** \\ FSE = 4 & 19/101 cases & 14 /101 cases & 8/101 cases \\ FSE = 3 & 17/101 cases & 3/101 cases & 5/101 cases \\ FSE = 2 & 4/101 cases & 3/101 cases & 2/101 cases \\ FSE = 1 & 0/101 cases & 1 case/101 cases & 3/101 cases \\ FSE = 0 & 1 case/101 cases & 6/101 cases & **0/101 cases** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the results in the DRISHTI-GS dataset with reproduced algorithms with FSE. model achieved a higher average IoU and better distribution than those of the two reproduced algorithms in the TMUEH dataset. Among these proposed algorithms, the segmentation results were affected by blood vessels in [58; 59; 61; 62], while the proposed approach avoided this effect with a morphological process.