Development and application of a fundus image quality assessment system based on computer vision technology

Abstract

Objective: To develop a fundus image quality assessment system based on computer vision technology and to verify its accuracy by comparing the results of artificial discrimination and using this system. Methods: The process of image evaluation was divided into four modules: fundus image preprocessing, fundus image quality evaluation, fundus image content detection and evaluation result output. The system was designed to automatically evaluate the image quality of each fundus image, identify the optic disc and macula, and judge whether the image was qualified or not according to the image quality discrimination rules. A total of 2 397 fundus images of 787 type 2 diabetes patients were selected as the test data set. The average age of the patients, including 384 males and 403 females, was (69.65±19.09) years old. The images were taken by the staff of community health service centers in Shanghai with a fundus camera. The fundus image quality assessment system was used to conduct quality control and classification of the data set. At the same time, 12 professional fundus picture readers were employed to conduct manual quality control and classification of this data set. The system quality control results and artificial quality discrimination results were compared and analyzed. Results: The fundus image quality assessment system automatically recognized left and right eyes and eye positions on the input fundus images. The quality control interface included four indicator lights, which respectively corresponded to the images with the optic disc or macula as the center of the left or right eye. Evaluation of each fundus image was completed within 1 second, and the results were automatically displayed on the user interface. The 2 397 fundus photos were identified manually as 1 846 qualified photos and 551 unqualified photos. Among the unqualified images, 62 (11.27%) were too dark, 51 (9.27%) were too bright, 59 (10.73%) were not clear in the macular area, 36 (6.54%) showed no macula or optic disc, 125 (22.73%) could not present the fundus structure, 175 (31.82%) were blurred, and 42 (7.64%) were blocked. The results of the system and manual assessment were consistent in 1 788 qualified images (96.86%) and 550 unqualified images (99.82%), with an overall consistency rate of 97.54%. Conclusion: The fundus image quality assessment system can achieve highly consistent results with the professional judgment of ophthalmologists and has the characteristics of objectivity. (Chin J Ophthalmol, 2020, 56:920-927).