Generic features for fundus image quality evaluation

Fundus image is important for the medical screening and diagnosis of variety ophthalmopathy. The effectiveness of such a process, however, depends very much on the quality of the fundus image captured. This paper aims to asses in real-time the quality of a fundus image by first extracting a multitude of generic features, including statistical characteristics, entropy, texture, symmetry, frequency components and blur metric, which is then followed by a support vector machine (SVM) trained to filter out the poor quality image for clinic usage. The method was tested on a dataset of 3224 images collected from an eye hospital's practical screening project in rural areas in Northeastern Region of China. With the detection rate achieved being 0.9308, the corresponding false alarm rate is 0.1127, and the overall accuracy is 0.9138. The area under an ROC curve is as high as 0.9619. It is shown that the fundus images of poor quality can be automatically detected on the spot to ensure a clinically meaningful ophthalmopathy screening and diagnosis by a human expert or even an artificial intelligence software.