Temporal registration for low-quality retinal images of the murine eye.

This paper presents an investigation into different approaches for segmentation-driven retinal image registration. This constitutes an intermediate step towards detecting changes occurring in the topography of blood vessels, which are caused by disease progression. A temporal dataset of retinal images was collected from small animals (i.e. mice). The perceived low quality of the dataset employed favoured the implementation of a simple registration approach that can cope with rotation, translation and scaling, in the presence of major vascular dissimilarities, distortions, noise, and blurring effects. The proposed approach uses a single control point, i.e. the centroid of the optic disc, and achieves accurate registration by matching points in the pair of input images using mean squared error calculation. A number of alternative, more sophisticated methods have been explored alongside the proposed one. While these other methods could prove valuable and perform reasonably well when applied on good quality images, they generally fail when using the dataset at hand.