Quantitative and qualitative image quality analysis of super resolution images from a low cost scanning laser ophthalmoscope

The lurking epidemic of eye diseases caused by diabetes and aging will put more than 130 million Americans at risk of blindness by 2020. Screening has been touted as a means to prevent blindness by identifying those individuals at risk. However, the cost of most of today's commercial retinal imaging devices makes their use economically impractical for mass screening. Thus, low cost devices are needed. With these devices, low cost often comes at the expense of image quality with high levels of noise and distortion hindering the clinical evaluation of those retinas. A software-based super resolution (SR) reconstruction methodology that produces images with improved resolution and quality from multiple low resolution (LR) observations is introduced. The LR images are taken with a low-cost Scanning Laser Ophthalmoscope (SLO). The non-redundant information of these LR images is combined to produce a single image in an implementation that also removes noise and imaging distortions while preserving fine blood vessels and small lesions. The feasibility of using the resulting SR images for screening of eye diseases was tested using quantitative and qualitative assessments. Qualitatively, expert image readers evaluated their ability of detecting clinically significant features on the SR images and compared their findings with those obtained from matching images of the same eyes taken with commercially available high-end cameras. Quantitatively, measures of image quality were calculated from SR images and compared to subject-matched images from a commercial fundus imager. Our results show that the SR images have indeed enough quality and spatial detail for screening purposes.