An automatic quantitative measurement method for performance assessment of retina image registration algorithms.

This paper presents a novel automatic quantitative measurement method for assessment of the performance of image registration algorithms designed for registering retina fundus images. To achieve automatic quantitative measurement, we propose the use of edges and edge dissimilarity measure for determining the performance of retina image registration algorithms. Our input is the registered pair of retina fundus images obtained using any of the existing retina image registration algorithms in the literature. To compute edge dissimilarity score, we propose an edge dissimilarity measure that we called "robustified Hausdorff distance". We show that our proposed approach is feasible as designed by drawing comparison to visual evaluation results when tested on images from the DRIVERA and G9 dataset.