Blood Vessel And Background Separation For Retinal Image Quality Assessment

Retinal image analysis has become an intuitive and standard aided diagnostic technique for eye diseases. The good image quality is essential support for doctors to provide timely and accurate disease diagnosis. This paper proposes an end-to-end learning based method for evaluating the retinal image quality. First, blood vessels of the input image are segmented by U-Net, and the fundus image is divided into two parts: blood vessels and background. Then, we design a dual branch network module which extracts global features that influence the image quality and suppress the interference of blood vessels and local textures to achieve better performance. The proposed module can be embedded in various advanced network structures. The experimental results show the more efficient convergence rate for the network with the module. The best network accuracy rate is 85.83%, the AUC is 0.9296, and the F1-score is 0.7967 on the collected local dataset. Additionally, the model generalization is tested on the public DRIMDB dataset. The accuracy, AUC, and F1-score reach 97.89%, 0.9978, and 0.9688, respectively. Compared with the state-of-the-art networks, the performance of the proposed method is proven to be accurate and effective for retinal image quality assessment.