# Optic Disc Detection from Fundus Photography via Best-Buddies Similarity
## 4 Discussion  
OD detection in fundus photography is an important prerequisite for the diagnosis and treatment of retinopathy. Accurate and early detection of ODs in retinal fundus images has been shown to be an initial and critical step in both ocular image analysis and computer-aided diagnosis. However, many OD detection methods often fail when the fundus images have lesions that are similar to the brightness of ODs. Vessel-based methods can take full advantage of information, such as the structure and orientation of blood vessels, to solve problems caused by easily confused lesions. However, these methods require a strict geometric template which depends on accurate image segmentation, rendering high computational complexity of the algorithms. The template-based matching techniques are capable of developing fast and robust OD detection algorithms, but these algorithms often require high computational cost and simple targets without nonrigid deformation and outliers. In this paper, a new method for the automated detection of OD from fundus photography is proposed, which is achieved by applying the BBS measure for template matching to deal with the problem of OD localization. First, the template and each of the candidate regions within the fundus image can be represented as two point sets. We then measure the similarity between the two sets of points by employing the BBS computed by counting the number of mutual nearest neighbors based on some statistical distance measures. Furthermore, we adjust the traditional BBS model by combining a gradient constraint term to improve the accuracy of distance measurements to capture the spatial distribution of patches in the templates and fundus images. The performance of our method was validated on the DRIVE and DIARETDB1 databases and obtained quantitative results.The success rate was 100% and 97.7% for the DRIVE and DIARETDB1 databases, and the average error distance between the estimated and the manually identified optic disc center was 10.4 and 12.9 pixel, respectively. The proposed method compared with other commonly used methods shows superior performance, with high localization success rates and fast localization speeds. The proposed method avoids the complex work of segmenting blood vessels and is robust against high levels of lesions in fundus images. Experiments were conducted on both the DRIVE and DIARETDB1 databases and show that our approach outperforms state-of-the-art methods. Future work will explore a novel multiple target localization model, which can be used to detect multiple retinal components such as vessels, drusens, and ODs. [PERSON] and [PERSON] conceived the proposed method of OD detection. [PERSON] and [PERSON] implemented the algorithm and performed the experiments. [PERSON] and [PERSON] wrote the paper. [PERSON] and [PERSON] contributed to the analysis and assessment of the results. National Nature Science Foundation of China (No. 61572300), the Natural Science Foundation of Shandong Province in China (ZR2017 BC013, ZR2014 FM001), and the Taishan Scholar Program of Shandong Province of China (No. : TSHW201502038). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Localization Methods** & **Dataset** & **Number of Images** &
\begin{tabular}{c} **Average Error** \\ **Distance (Pixels)** \\ \end{tabular} & **Success Rate** \\ \hline [PERSON] et al. [12] & DRIVE & 40 & - & 60\% \\ [PERSON] et al. [28] & DRIVE & 40 & - & 80\% \\ [PERSON] et al. [29] & DRIVE & 40 & 15.9 & 95\% \\ [PERSON] et al. [30] & DRIVE & 40 & 23.2 & 100\% \\ [PERSON] et al. [31] & DRIVE & 40 & 27 & 100\% \\ [PERSON] et al. [14] & DRIVE & 40 & - & 95\% \\  & DIARETDB1 & 89 & - & 94.38\% \\ [PERSON] and [PERSON]. [32] & DRIVE & 40 & - & 100\% \\  & DIARETDB1 & 89 & - & 96.62\% \\ The proposed method & DRIVE & 40 & 10.4 & 100\% \\  & DIARETDB1 & 89 & 12.9 & 97.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of different algorithms. **Acknowledgments: This work is supported by the National Nature Science Foundation of China (No.