A STRUCTURE-BASED REGION DETECTOR FOR RETINAL IMAGE REGISTRATION

A fundamental problem in retinal image registration (RIR) is the determination of correspondences between image pairs. State-of-the-art RIR methods use local features like scale invariant feature transform (SIFT) to find corresponding points. However, SIFT suffers from the quantity and quality of the detected points. On the other hand, the attention of human visual systems directs to regions instead of points for feature matching. Being aware of these issues, this paper presents a new structure-based region detector and describes a robust RIR framework. It is based on a robust watershed segmentation which obtains closed-boundary regions within a clean vascular structure map. Since vascular structure maps are relatively stable in partially overlapping and temporal image pairs, the regions are unaffected by viewpoint, content and illumination variations. The regions are approximated by convex polygons, so that robust boundary descriptors are achieved to match them. Experimental results on different datasets show that our approach is comparable or superior to SIFT-based methods in terms of efficiency, accuracy and speed.