# Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images
## 4 Experiment
### Parameters
#### 4.4.2 Discussion
A postprocessing convex hull was utilized in [61]; thus, it was also affected by dark noise. The effect of PPA also existed in [59]. The segmentation results in [60] were mainly affected by bright noise. However, the proposed approach was able to avoid the effect of PPA and bright noise with a suitable initial value and the constraint of the shape model. Due to the low contrast and effect of bright noise, both [31] and [18] suffered from segmentation failures, while the proposed model was based on the area and achieved better performance in low-contrast cases. In addition, the displayed segmentation results in [57] were not enough; the reason why the segmentation results were affected is not clear. As shown in Table 9, in the DRISHTI-GS dataset, the proposed algorithm also achieved the best average FSE, with 5 points being the most and 0 points being the least. As shown in Table 10, in the TMUEH dataset with the best average FSE, the most cases with 3 or more points and the least cases with 0 points were obtained. As shown in Table 4, the segmentation results in the DRISHTI-GS dataset were significantly more accurate than those in the TMUEH dataset. There are two main reasons for this:  
1. Different light sources were used when posterior and wide-angle fundus images are taken. Posterior fundus images use white light sources, while wide-angle fundus images utilize red and green light sources. The proposed method utilizes the value channel in the HSV color space to segment the OD. The value channel is the maximal value of the red, green, and blue of these three channels. However, there is no blue channel information in wide-angle fundus images, which may reduce the segmentation accuracy. 2. The resolution of the ROI on posterior fundus images is about \(600\times 600\), while the resolution of the ROI on wide-angle fundus images is about \(200\times 200\). The low resolution of wide-angle fundus images may also be one of the reasons for the low segmentation accuracy. However, as shown in Figure 25, there are still some problems that cannot be solved with multiple preprocessing HLSM:  
1. As shown in Figure 25a, the existence of too-strong blood vessels causes oversegmentation. Because the blood vessels are too thick or multiple blood vessels are entangling, there are still dark shadows after noise removal. The pixel values covered by blood vessels were lower than those in other areas, resulting in undersegmentation. 2. As shown in Figure 25b, if there is a large area of bright noise around the OD, the OD is also undersegmented. This situation is predictable, since the proposed method is an area-based level set model, and the initial value is based on thresholding. 3. As shown in Figure 25c, the brightness of the ring area (the area between the OD and OC boundaries) was too low, which caused a large error in the initial-value detection, resulting in oversegmentation. As shown in Figure 26, some segmentation results of representative cases were compared: from a threshold-based method [18], an active contour-based method [31], and the proposed approach. The proposed multiple preprocessing HLSM was proposed to mainly solve the problems of OD segmentation being affected by PPA and bright noise. The other approaches could not achieve ideal results. In addition, problems such as the low brightness of the ring area and large bright noise also cannot be solved by other methods. Therefore, the segmentation results demonstrate that the proposed approach improved the accuracy of the OD segmentation results.