A deep retinal image quality assessment network with salient structure priors

Ziwen Xu, Beiji Zou, Qing Liu

Abstract: Retinal image quality assessment is an essential prerequisite for diagnosis of retinal diseases. Its goal is to identify high quality retinal images in which anatomic structures and lesions attracting ophthalmologists’attention most are exhibited clearly and definitely while reject poor quality images. Motivated by this, we mimic the way that ophthalmologists assess the quality of retinal images and propose a method termed SalStructuIQA. First, two salient structures are detected for retinal quality assessment. One is large-size salient structures including optic disc and exudates in large-size. The other is tiny-size salient structures mainly including vessels. Then the proposed two salient structure priors are incorporated with deep convolutional neural network (CNN) to enforce CNN pay attention to these salient structures. Accordingly, two CNN architectures named Dual-branch SalStructIQA and Single-branch SalStructIQA are designed for the incorporation, respectively. Experimental results show that the Fscore of our proposed Dual-branch SalStructIQA and Single-branch SalStructIQA are 0.8723 and 0.8662 respectively on the public Eye-Quality dataset, which demonstrates the effectiveness of our methods.