Automatic fundus image quality assessment on a continuous scale

Abstract

Fundus photography is commonly used for screening, diagnosis, and monitoring of various diseases affecting the eye. In addition, it has shown promise in the diagnosis of brain diseases and evaluation of cardiovascular risk factors. Good image quality is important if diagnosis is to be accurate and timely. Here, we propose a method that automatically grades image quality on a continuous scale which is more flexible than binary quality classification. The method utilizes random forest regression models trained on image features discovered automatically by combining basic image filters using simulated annealing as well as features extracted with the discrete Fourier transform. The method was developed and tested on images from two different fundus camera models. The quality of those images was rated on a continuous scale from 0.0 to 1.0 by five experts. In addition, the method was tested on DRIMDB, a publicly available dataset with binary quality ratings. On the DRIMDB dataset the method achieves an accuracy of 0.981, sensitivity of 0.993 and specificity of 0.958 which is consistent with the state of the art. When evaluating image quality on a continuous scale the method outperforms human raters.