Feature-based registration of retinal images.

Registration of retinal images taken at different times frequently is required to measure changes caused by disease or to document retinal location of visual stimuli. Cross-correlation has been used previously for such registration, but it is computationally intensive. We have modified a faster algorithm, sequential similarity detection (SSD), to use only the portion of the template that contains retinal vessels. When compared to standard SSD and cross-correlation, this modification improves the reliability of detection for a variety of retinal imaging modalities. The improved reliability enables implementation of a two-stage registration strategy that further decreases the amount of computation and increases the speed of registration.