# Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images
## 3 Multiple Preprocessing Hybrid Level Set Model
### Multiple Preprocessing
#### 3.2.1 Region of Interest (ROI) Detection  
OD only occupies a small part of fundus images; in order to speed up OD segmentation and reduce the effect of noise, it is necessary to locate the OD and detect the ROI. The OD is an oval-shaped area with high vascular density and high brightness in fundus images, and these characteristics can be used to accurately locate OD [48]. The side length of the extracted ROI is 3 times the average diameter of OD. Because there is no guarantee that the center of OD would be detected, and there are differences in the size of the OD, it is  
Figure 7: Comparison of shape-based term is used and not used. (blue contour: the segmentation result, green contour: the ground-truth of OD).  
necessary to leave free space around the OD. Therefore, the size of the ROI is 3 times the average diameter of the OD. The flowchart of ROI detection is shown in Figure 8, the results of each process in posterior fundus images are shown in Figure 9, and the results of each process in wide-angle fundus images are presented in Figure 10. In wide-angle fundus images, a circular mask image (Figure 10a) is used to avoid the influence of eyelashes and the fundus camera wall. Each process of ROI detection is explained in detail as follows.  
* Step 1: Extracting the green channel (Figures 9b and 10b) from the RGB color space and inverting it (Figures 9c and 10c). In order to take advantage of the feature of high vascular density, it is necessary to roughly segment the blood vessels. In fundus images, blood vessels have a high contrast in the green channel, which is why the green channel is extracted. However, blood vessels are darker than those in other areas in the green channel. Thus, for using morphological top-hat transformation to segment blood vessels, the green channel is inverted.
* Step 2: Using morphological top-hat transformation to segment blood vessels (Figures 9d and 10d). Although the obtained blood-vessel map is not clear and accurate, it is fast and adequate enough to find the areas with high vascular density.
* Step 3: Finding several circular areas with the highest vascular density (Figures 9e and 10e). The radius of circular areas is the average OD radius. Because of the rough and imprecise blood-vessel map, selecting only a few areas may not include OD. Thus, 20 circular areas were selected in this paper.
* Step 4: Selecting one area (Figures 9f and 10f) which has the largest number of 2% brightest pixels in value channel. The purpose of this step is to utilize the characteristic of high brightness.
* Step 5: Extracting the rough ROI (Figures 9g and 10g) by this circular area. The side length of rough ROI is 4 times the average diameter of OD and the center of rough ROI is the center of circular area. As shown in Figure 11, there was still an error (the OD was not located at the center of ROI image) if locating OD only on the basis of high vascular density and high brightness.
* Step 6: Correcting the ROI (Figures 9h and 10h) by Hough Circular Transform from rough ROI. This step uses the characteristic of oval-shaped. The side length of corrected ROI is 3 times the average diameter of OD.  
Figure 8: Flowchart of ROI detection: (**a–i**) correspond to images in Figures 9 and 10.  
Figure 10: Cont.  
Figure 9: Results of ROI detection in posterior fundus images.