Abstract
This paper presents a novel two step approach for longitudinal (over time) registration of retinal images. Longitudinal registration is an important preliminary step to analyse longitudinal changes on the retina including disease progression. While potential overlap and minimal geometric distortion are likely in longitudinal images, identification of reliable features over time is a potential challenge for longitudinal registration. Relying on the widely accepted phenomenon that retinal vessels are more reliable over time, the proposed method aims to accurately match bifurcation and crossover points between different timestamp images. Binary robust independent elementary features (BRIEF) are computed around bifurcation points which are then matched based on Hamming distance. Prior to computing BRIEF descriptors, a preliminary registration is performed relying on SURF key-point matching. Experiments are conducted on different image datasets containing 109 longitudinal image pairs in total. The proposed method has been found to produce accurate registration (i.e. registration with zero alignment error) for 97 % cases, which is significantly higher than the other methods in comparison. The paper also reveals the finding that both the number and distributions of accurately matching key-points pairs are important for successful registration of image pairs.
