# Optic Disc Detection from Fundus Photography via Best-Buddies Similarity
## 1 Introduction  
The optic disc (OD) is commonly considered one of the main features of a retinal fundus image. Accurate and early OD detection has been shown to be very important in ocular image analysis and computer-aided diagnosis. On the one hand, the change in the shape, color, or location of ODs is a critical indicator of various blinding eye diseases including glaucoma, cataract, and diabetic retinopathy [1; 2; 3]. On the other hand, OD detection is typically a key preprocessing component in computer algorithms developed for the automatic characterization of retinal anatomical structures (e.g., retinal vessels and the macula), which is helpful for aiding the ophthalmologist in determining the position of many retinal abnormalities such as exudates, drusen, and microaneurysms. OD detection aims to find the location and area of ODs in retinal fundus images. In general, an OD has a very different appearance compared with the rest of the retina. It usually appears as ayellowish, circular, or slightly oval region brighter than the surrounding in fundus images. An OD is the exit point of ganglion cell axons leaving the eye, roughly one-sixth the width of the image in diameter [4; 5; 6; 7]. Localizing the OD is the initial step of most vessel segmentation, disease diagnostic, and retinal recognition algorithms. It often works as a landmark and reference for the other features in fundus images [8; 9; 10]. However, because of the presence of eye diseases, light, and noise, the accurate detection of ODs is a challenging task and is the subject of much research. There are many studies related to the detection of the OD from fundus photography. Based on the different properties of OD presented in the fundus images, methods of OD detection can be classified into different categories, such as appearance-based methods and vessel-based methods [5; 11]. Due to the obvious round and bright area of the OD, and since the region of the OD in fundus photography is fixed, some scholars have proposed methods to detect ODs based on appearance features. These researchers were focused on areas in fundus images where the brightness is highest. There are many appearance-based methods. [PERSON] detected regions with the highest variation in the intensity of adjacent pixels between the vessels and the bright nerve fibers. Such regions are the result of this method, which are ODs [12; 13]. [PERSON] detected ODs in fundus images by using key point detection with SURF, texture analysis, and visual dictionary techniques on bright regions [14]. [PERSON] and [PERSON] detected ODs by template matching. [PERSON] used pyramidal decomposition and Hausdorff-based template matching in low-resolution fundus images. [PERSON] had chosen the average of the color-normalized OD region in 25 retinal images as the template image [15; 16; 17]. [PERSON] found the brightest pixels in the fundus image, combined them with the clustering algorithm to segment the candidate area of the OD, and then used principal component analysis (PCA) to find the OD [18]. Vessel-based methods depend mainly upon analyzing the structure and the density of retinal vessels. [PERSON] and others detected ODs by analyzing the structure and the direction of the vessels, as well as the degree of template matching between the given blood vessel directions [19]. [PERSON] et al. proposed a comprehensive utilization of the direction of vessels, the converged blood vessels, and the brightness information of ODs. They found that the blood vessels in the area mostly extend along the vertical direction. Therefore, the vertical gradient component is greater than the horizontal gradient component in the region's edge gradient. The total edge gradient in this region is greater than other regions. By the two projections, the center coordinates of ODs are located [20; 21]. [PERSON] proposed a geometric model of blood vessel structures to locate ODs.