Evaluation of Retinal Image Gradability by Image Features Classification

In this paper is presented a retinal image quality evaluation algorithm that classifies images into gradable and ungradable categories. The algorithm is based on the information of four retinal image quality indicators: colour, focus, contrast and illumination. Beyond being the base of the overall retinal image quality classification, these four indicators also provide important information to a fundus camera operator who can use it to better adjust the image capture process. The overall algorithm performance was evaluated through comparison against human-made classification revealing a sensitivity of 97.41% and a specificity of 99.49% in a dataset with 2032 retinal images, collated from a range of different sources, including DRIVE, Messidor, ROC and STARE datasets.