Super-Resolved Retinal Image Mosaicing

TL;DR

This paper introduces an automatic method to create high-resolution, wide-field retinal images by combining multiple low-resolution videos, using eye tracking, super-resolution, and novel registration techniques, suitable for low-cost mobile cameras.

Contribution

It presents a fully automatic framework that reconstructs high-resolution retinal mosaics from low-cost mobile camera videos, incorporating eye motion tracking and a new polynomial registration scheme.

Findings

Reconstructed retinal images with up to 30° FOV from 10 views.

Clinically usable mosaics validated by human experts.

Quantitative comparison shows improved image quality over conventional methods.

Abstract

The acquisition of high-resolution retinal fundus images with a large field of view (FOV) is challenging due to technological, physiological and economic reasons. This paper proposes a fully automatic framework to reconstruct retinal images of high spatial resolution and increased FOV from multiple low-resolution images captured with non-mydriatic, mobile and video-capable but low-cost cameras. Within the scope of one examination, we scan different regions on the retina by exploiting eye motion conducted by a patient guidance. Appropriate views for our mosaicing method are selected based on optic disk tracking to trace eye movements. For each view, one super-resolved image is reconstructed by fusion of multiple video frames. Finally, all super-resolved views are registered to a common reference using a novel polynomial registration scheme and combined by means of image mosaicing. We evaluated our framework for a mobile and low-cost video fundus camera. In our experiments, we reconstructed retinal images of up to 30{\deg} FOV from 10 complementary views of 15{\deg} FOV. An evaluation of the mosaics by human experts as well as a quantitative comparison to conventional color fundus images encourage the clinical usability of our framework.