  Retinal image quality assessment is an essential prerequisite for diagnosis
of retinal diseases. Its goal is to identify retinal images in which anatomic
structures and lesions attracting ophthalmologists' attention most are
exhibited clearly and definitely while reject poor quality fundus 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 for automated retinal quality assessment. One is the large-size
salient structures including optic disc region and exudates in large-size. The
other is the tiny-size salient structures which mainly include vessels. Then we
incorporate the proposed two salient structure priors with deep convolutional
neural network (CNN) to shift the focus of CNN to salient structures.
Accordingly, we develop two CNN architectures: Dual-branch SalStructIQA and
Single-branch SalStructIQA. Dual-branch SalStructIQA contains two CNN branches
and one is guided by large-size salient structures while the other is guided by
tiny-size salient structures. Single-branch SalStructIQA contains one CNN
branch, which is guided by the concatenation of salient structures in both
large-size and tiny-size. Experimental results on Eye-Quality dataset show that
our proposed Dual-branch SalStructIQA outperforms the state-of-the-art methods
for retinal image quality assessment and Single-branch SalStructIQA is much
light-weight comparing with state-of-the-art deep retinal image quality
assessment methods and still achieves competitive performances.
