# Optic Disc Detection from Fundus Photography via Best-Buddies Similarity
## 1 Introduction
They used two opposite directions parabolas to describe the direction of blood vessels, because the two parabolic common vertices are located in the area of the ODs, and they detected ODs through this feature [22; 23]. Both of these methods have their strengths and weaknesses. Appearance-based methods make full use of the visual features of OD such as brightness and texture. Some researchers think that the method of detecting ODs by their appearance is effective, but these methods often fail when the fundus images have lesions and the lesions are similar to the brightness of ODs, and these methods may have high computational complexity. Vessel-based methods take full advantage of information such as the structure and orientation of blood vessels and solve the detection problem caused by the disease. However, these methods require a strict geometric template, and the formulation of the template depends on accurate image segmentation, which leads to a highly complex algorithm and an unsatisfied real-time requirement of OD detection. Thus, in the process of pattern recognition, we should pay more attention to local information [24; 25]. To address these problems, many studies have demonstrated that template-based matching can be used for developing faster and more robust methods for OD detection [17; 19]. In the traditional template matching process, all the points in the candidate window are used for the computation of similarity. Nevertheless, these algorithms may have high computational cost and match the error area when the interested target area has nonrigid deformation and outliers. In this paper, we propose a new method for the automated detection of ODs from fundus photography. It is carried out by applying the Best-Buddies Similarity (BBS) measure based on template matching to address the problem of OD localization [26]. Specifically, we first represent both the template and each of the candidate regions within the fundus image as two point sets. We think that only similar points in the template image and the candidate window should be considered. The BBS is then employed to measure the similarity between the two sets of points, which is computed by counting the number of mutual nearest neighbors based on statistical distance measures. For capturing the spatial distribution of patches in the templates and fundus images, we adjusted the traditional BBS model by introducing a gradient constraint term to improve the accuracy of distance measurements. Our method avoids the complex work of segmenting blood vessels and strengthens the robustness against high levels of lesions in fundus images. The experimental results show that our method has lower computational complexity and performs well when the fundus images have lesions. Experiments were conducted with both the Digital Retinal Images for Vessel Extraction (DRIVE) and Standard Diabetic Retinopathy Database Calibration Level 1 (DIARETDB1) databases, and results show that our approach outperforms state-of-the-art methods. The rest of this paper is organized as follows. The Materials and Methods section describes the proposed methodology for the detection of ODs. The Results section presents the test design and the experimental results analysis. The Discussion section is devoted to concluding remarks and suggests future work.