## 4 Experiments and Results
### Cornea Segmentation Results
These samples demonstrate that when the cornea boundary is clearly visible in the ocular staining image, all methods accurately segment the cornea area, as evidenced in samples (a), (b), and (c). However, when the cornea boundary is unclear, as in samples (e), (f), (g), and (h), the segmentation performance varies among the models. The U-Net-ResNet-50 model effectively segments clearly defined areas, but exhibits reduced performance in regions with complex and ambiguous image features. In contrast, the high-resolution U-Net model provides excessive detail, resulting in significant oversegmentation. The SLIT-Net and SCED models yield results with comparable shapes, although SLIT-Net demonstrates lower performance. Moreover, the SLIT-Net model significantly underperforms compared to the U-Net model. This discrepancy may be due to the differences in the training dataset. In this experiment, the U-Net model was trained and validated on the SUSTech-SYSU dataset, whereas the SLIT-Net model was trained on a different dataset and subsequently validated on the SUSTech-SYSU dataset for comparison. The SCED and EM-5-SCED models demonstrate superior performance in identifying the overall cornea area, which is suitable for subsequent circle detection tasks, despite some segmentation errors. The segmentation results observed in these sample images align with the performance comparisons presented in Table 3. Furthermore, we compared the segmentation results from the SCED model with the SCED-HT method, which converts the segmented area to a circular shape. When the segmentation results from SCED accurately represent the cornea areas or resemble a circular shape, as in samples (a), (b), and (c), the outputs of SCED and SCED-HT are similar. However, when the segmented results are approximately circular but include small excess areas, as in samples (d) and (e), SCED-HT can remove excess areas and identify more accurate cornea regions compared to SCED. In cases such as samples (f) and (g), where the segmented areas are irregularly shaped, using the segmentation results from SCED directly may yield better outcomes than applying the HCT to determine the boundary of the cornea region. Finally, we compared the cornea segmentation results from the proposed method with other methods, as shown in Table 4. The details of each method are as follows:  
1. [leftmargin=*]
2. CANNY and RANSAC: In this method, the original ocular staining images were first converted into grayscale, then Canny Edge Detection [47] was applied to identify the cornea edges. Next, RANdom SAmple Consensus (RANSAC) [48] was used to detect circles from the edge images. 3. CANNY and HCT: The same procedure as in the first method, except with the HCT used for circle detection. The circle with the highest accumulator value was selected as the output. 4. EM-5-SCED and RADON: First, the cornea segments were determined from the ocular staining images by using the EM-5-SCED model and extracting the contour of the \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Accuracy** & **Micro IoU** & **MAPE (Cornea Size)** \\ \hline Image Processing Method [11] & 88.53 & 66.89 & 25.26 \(\pm\) 34.34 \\ SLIT-Net [45] & 94.10 & 82.02 & 14.64 \(\pm\) 20.54 \\ U-Net & 95.99 & 87.24 & 9.28 \(\pm\) 16.17 \\ U-Net-ResNet-50 & 95.95 & 86.85 & 9.74 \(\pm\) 16.30 \\ EM-5-SCED & 97.08 & 90.58 & 6.93 \(\pm\) 11.19 \\ EM-5-SCED-HT & 96.70 & 89.23 & 8.14 \(\pm\) 11.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the cornea segmentation performance across various methods. cornea segments. Then, Radon Transform-based circle detection was applied to detect the circles, as proposed by [PERSON] et al. [49]. * EM-5-SCED and RANSAC: The same procedure as the third method, except with RANSAC used for circle detection. * PROPOSED without \(F_{\alpha}(C)\): Our proposed approach utilizes HCT to detect circles from the contour of the cornea segments.