The paper describes a fast feature-based algorithm for accurately estimating the absolute location of a surgical tool (a laser), pointed at the curved human retina, from a series of image frames. The method is capable of a 91% success rate with just a third of the feature extraction computation, taking 37 ms overall per image on a 900 MHz Pentium III. The success rate approaches 100% when the feature extraction is allowed to run to completion. The median error is 0.92 pixels with 512×512 8-bit image frames. Making a significant break from prior incremental tracking-based efforts, we propose a framework that involves extensive offline precomputation to build a "spatial map" and high-speed online "spatial referencing" to rapidly register each surgical image with this spatial map. The spatial referencing technique is designed around the idea of quasi-invariant indexing. Similarity invariants, locally valid despite the curved nature of the retina, are computed from constellations of vascular landmarks. These are detected using a high-speed algorithm that recursively traces the blood vessel structure. Invariant indexing establishes initial correspondences between landmarks from the online image and landmarks stored in the spatial map. Alignment and verification steps gradually extend the similarity transformation computed from these initial correspondences to a global, high-order transformation. The spatial map is precomputed to contain mosaics, distance maps and an invariant database, all designed to make these spatial referencing computations extremely fast.
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