# Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images
## 4 Experiment
### Parameters  
The parameters of the proposed HLSM are shown in Table 3. The main parameters are the weights of each energy functional term. The average radius of OD is used in ROI detection, which is used to determine the size of an ROI image. \begin{table}
\begin{tabular}{c l} \hline \hline
**Score** & **Criteria** \\ \hline
0 & There are obvious errors in the boundaries of the four sides. \\
1 & There are errors in the boundaries of four sides, but better than 0. \\
2 & Only one side is accurate enough * \\
3 & Two sides are accurate enough. \\
4 & Three sides are accurate enough. \\
5 & All sides are accurate enough. \\ \hline \hline \end{tabular}  
* Accurate enough: according to the subjective evaluation of clinician, it would not affect the subsequent diagnosis.  
\end{table} 
Table 2: The evaluation criteria of FSE. Figure 22: The four parts of OD. Figure 24: Segmentation result examples in TMUEH dataset (green contour: GT, blue contour: segmentation results) \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **DRISHTI-GS** & **TMUEH** \\ \hline Maximal IoU & 0.9767 & 0.9300 \\ Minimal IoU & 0.5933 & 0.5205 \\ Average IoU & 0.9275 & 0.8179 \\ Variance in IoU & 0.0025 & 0.0104 \\
0.9 \(\leq\) IoU & 88/101 cases & 8/37 cases \\
0.8 \(\leq\) IoU \(<\) 0.9 & 11/101 cases & 20/37 cases \\ IoU \(<\) 0.8 & 2/101 cases & 9/37 cases \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation results with IoU. As shown in Table 6, the results of IoU evaluation in the DRISHTI-GS dataset were compared with those in other algorithms. In [57; 58; 59], the U-Net architecture was used to segment OD. The authors in [60] used a boundary and entropy-driven adversarial learning-based deeplabv3+ (BEAL-Deeplabv4+) architecture to segment the OD. In [61], entropy information was also used; the architecture was an entropy sampling- and ensemble learning-based CNN (EE-CNN). In [62], many common non-deep-learning methods were reproduced to segment the OD. A method called level set adaptively regularized kernel-based intuitive fuzzy C means (LARKIFCM) was also proposed to segment OD. In order to compare the OD segmentation results in the TMUEH dataset and the results with FSE evaluation method, two algorithms from other papers were reproduced: one using the active contour-based method [31], and the other using the threshold-based method [18]. The proposed ROI detection method was used when reproducing these two methods. The segmentation results evaluated with IoU are displayed in Tables 7 and 8, and the results evaluated with FSE are shown in Tables 9 and 10. \begin{table}
\begin{tabular}{c c} \hline
**Approaches** & **Average IoU** \\ \hline U-Net [57] & 0.8900 \\ BEAL-Deeplabv4+ [60] & 0.8620 \\ LARKIFCM [62] & 0.9100 \\ U-Net [58] & 0.9187 \\ EE-CNN [61] & 0.9140 \\ U-Net [59] & 0.9062 \\ Proposed & **0.9275** \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of results in the DRISHTI-GS dataset with those in other papers with average IoU. \begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **Active** & **Threshold-** & **Proposed** \\  & **Contour-Based**[31] & **Based**[18] & **Proposed** \\ \hline Maximal IoU & 0.9695 & 0.9720 & **0.9767** \\ Minimal IoU & 0 & 0 & 0.5933 \\ Average IoU & 0.8757 & 0.8760 & **0.9275** \\ Variance in IoU & 0.0149 & 0.0255 & **0.0025** \\
0.9 \(\leq\) IoU & 58/101 cases & 74 /101 cases & **88/101 cases** \\
0.8 \(\leq\) IoU\(<\) 0.9 & 29 /101 cases & 17/101 cases & 11/101 cases \\ IoU\(<\) 0.8 & 14/101 cases & 10/101 cases & **2/101 cases** \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison of results in the DRISHTI-GS dataset with reproduced algorithms with IoU. \begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **DRISHTI-GS** & **TMUEH** \\ \hline Average FSE & 4.6436 & 3.5946 \\ FSE = 5 & 83/101 cases & 12/37 cases \\ FSE = 4 & 8/101 cases & 10/37 cases \\ FSE = 3 & 5/101 cases & 7 /37 cases \\ FSE = 2 & 2/101 cases & 5/37 cases \\ FSE = 1 & 3/101 cases & 2/37 cases \\ FSE = 0 & 0/101 cases & 1 case/37 cases \\ \hline \end{tabular}
\end{table}