Retinal Image Quality Classification Using Neurobiological Models of the Human Visual System

Retinal image quality assessment (IQA) algorithms use dif- ferent hand crafted features without considering the important role of the human visual system (HVS). We solve the IQA problem using the principles behind the working of the HVS. Unsupervised information from local saliency maps and supervised information from trained con- volutional neural networks (CNNs) are combined to make a final decision on image quality. A novel algorithm is proposed that calculates saliency values for every image pixel at multiple scales to capture global and local image information. This extracts generalized image information in an un- supervised manner while CNNs provide a principled approach to feature learning without the need to define hand-crafted features. The individ- ual classification decisions are fused by weighting them according to their confidence scores. Experimental results on real datasets demonstrate the superior performance of our proposed algorithm over competing meth- ods.