Super-Resolved Retinal Image Mosaicing
This addresses the problem of limited and costly retinal imaging for medical diagnostics, offering a practical solution with incremental improvements in mosaicing and super-resolution techniques.
The paper tackles the challenge of acquiring high-resolution retinal fundus images with a large field of view by proposing an automatic framework that reconstructs such images from multiple low-resolution captures using a low-cost camera, achieving up to 30° FOV from 10 views of 15° FOV and showing clinical usability through expert evaluation and quantitative comparison.
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° FOV from 10 complementary views of 15° 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.