# Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images
###### Abstract  
The accurate segmentation of the optic disc (OD) in fundus images is a crucial step for the analysis of many retinal diseases. However, because of problems such as vascular occlusion, parapapillary atrophy (PPA), and low contrast, accurate OD segmentation is still a challenging task. Therefore, this paper proposes a multiple preprocessing hybrid level set model (HLSM) based on area and shape for OD segmentation. The area-based term represents the difference of average pixel values between the inside and outside of a contour, while the shape-based term measures the distance between a prior shape model and the contour. The average intersection over union (IoU) of the proposed method was 0.9275, and the average four-side evaluation (FSE) was 4.6426 on a public dataset with narrow-angle fundus images. The IoU was 0.8179 and the average FSE was 3.5946 on a wide-angle fundus image dataset compiled from a hospital. The results indicate that the proposed multiple preprocessing HLSM is effective in OD segmentation.  
optic disc segmentation; multiple preprocessing; hybrid level set wide-angle fundus images; four-side evaluation 20  
This paper proposes a multiple preprocessing hybrid level set model (HLSM) to solve the problems in which OD segmentation is affected by bright noise and PPA. The contour in HLSM is mainly controlled by area-based and shape-based terms. The area-based term represents the difference of the average pixel values between the inside and outside of the contour, while the shape-based term measures the distance between a prior shape model and the contour. Moreover, this paper proposes a novel evaluation method of four-side evaluation (FSE), and addresses the limitation of objective evaluation methods such as intersection over union (IoU) in over- and undersegmentation around the entire OD. Furthermore, the FSE is a subjective evaluation from clinicians that can prove whether the segmentation results are accurate enough in the view of clinician. The effectiveness of the proposed approach is verified in two types of fundus image, posterior and wide-angle.  
Figure 1: Example of posterior fundus image.  
Figure 3: Problems in OD segmentation.  
Figure 2: Example of wide-angle fundus image.  
The proposed method achieved results with an average IoU of 0.9275 and an average FSE of 4.6426 in the DRISHTI-GS posterior fundus image dataset, and an average IoU of 0.8179 and average FSE of 3.5946 in the TMUEH wide-angle fundus image dataset.  
The contributions of this paper are as follows:  
1. The proposed method has higher robustness that could successfully segment the OD in both the posterior and wide-angle fundus images.
2. The effect of PPA and bright noise causes undersegmentation, and the OD being occluded by blood vessels causes undersegmentation. The proposed approach achieves high accuracy in OD segmentation results by solving these problems.
3. A new evaluation method, FSE, is proposed for clinicians to subjectively evaluate OD segmentation results.  
This paper is composed as follows: Section 2 briefly introduces some research background on OD segmentation in fundus images; Section 3 proposes and explains the multiple preprocessing hybrid level set model; Section 4 outlines the experiment and discussion; Section 5 draws some conclusions.