This paper describes computer vision algorithms to assist in retinal laser surgery, which is widely used to treat leading blindness causing conditions but only has a 50% success rate, mostly due to a lack of spatial mapping and reckoning capabilities in current instruments. The novel technique described here automatically constructs a composite (mosaic) image of the retina from a sequence of incomplete views. This mosaic will be useful to ophthalmologists for both diagnosis and surgery. The new technique goes beyond published methods in both the medical and computer vision literatures because it is fully automated, models the patient-dependent curvature of the retina, handles large interframe motions, and does not require calibration. At the heart of the technique is a 12-parameter image transformation model derived by modeling the retina as a quadratic surface and assuming a weak perspective camera, and rigid motion. Estimating the parameters of this transformation model requires robustness to unmatchable image features and mismatches between features caused by large interframe motions. The described estimation technique is a hierarchy of models and methods: the initial match set is pruned based on a 0th order transformation estimated using a similarity-weighted histogram; a 1st order affine transformation is estimated using the reduced match set and least-median of squares; and the final, 2nd order 12-parameter transformation is estimated using an M-estimator initialized from the 1st order results. Initial experimental results show the method to be robust and accurate in accounting for the unknown retinal curvature in a fully automatic manner while preserving image details.
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