A Novel Hybrid Optic Disc Detection and Fovea Localization Method Integrating Region-Based Convnet and Mathematical Approach

Optic disc (OD), fovea, and blood vessels are major anatomical structures in a fundus image. In retinal image processing, automated detection of structural parts is crucial in analyzing image patterns and abnormalities caused by eye diseases such as glaucoma, macular edema, and diabetic retinopathy. This paper presents a robust and efficient OD detection and fovea localization method integrating region-based deep convolutional neural networks and a mathematical approach. The proposed model consists of two stages: In the first stage, we generated multiple OD region proposals and then detected the OD based on the boundary box with the highest score using Faster R-CNN. In the second stage, we calculated the localization of the fovea employing a mathematical model considering the coordinates of the predicted OD region. We used four publically available fundus image databases, ORIGA-light, DRIVE, DIARET-DB1, and MESSIDOR, to evaluate our model. The proposed hybrid model was trained with 70% images of the ORIGA-light database and tested on 30% images of ORIGA-light and all images of the other databases. To show the robustness of the model, all databases were divided into two parts, as normal and diseased samples. The presented model achieved a reliable and flexible performance in detecting the OD, with overall IoU results of 88.5, 75.5, 84.4, and 86.8% on ORIGA-light, DRIVE, DIARET-DB1, and MESSIDOR databases, respectively. Moreover, the average fovea localization results in terms of IoU were 58.1, 66.1, 71, and 73.2% on ORIGA-light, DRIVE, DIARET-DB1, and MESSIDOR databases, respectively. The experimental tests demonstrate that the proposed approach achieves promising results for both normal and diseased images.