Background and objectives: Measurements of the cyclotorsional movement of the eye are crucial in refractive surgery procedures. The planned surgery pattern may vary substantially during an operation because of the position and eye movements of the patient. Since these factors affect the outcome of an operation, eye registration methods are applied in order to compensate for errors. While the majority of applications are based on features of the iris, we propose a registration method which uses scleral blood vessels. Unlike previous offline techniques, the proposed method is applicable during surgery.
Methods: The sensitivity of the proposed registration method is tested on an artificial benchmark dataset involving five eye models and 46,305 instances of eye images. The cyclotorsion angles of the dataset vary between -10° and +10° at 1° intervals. Repeated measurements and ANOVA and Cochran's Q tests are applied in order to determine the significance of the proposed method. Additionally, a pilot study is carried out using data obtained from a commercially available device. The real data are validated using manual marking by an expert.
Results and conclusions: The results confirm that the proposed method produces a smaller error rate (mean = 0.44 ± 0.41) compared to the existing method in [1] (mean = 0.64 ± 0.58). A further conclusion is that feature extraction algorithms affect the results of the proposed method. The SIFT (mean = 0.74 ± 0.78), SURF64 (mean = 0.56 ± 0.46), SURF128 (mean = 0.57 ± 0.48) and ASIFT (mean = 0.29 ± 0.25) feature extraction algorithms were examined; the ASIFT method was the most successful of these algorithms. Scleral blood vessels are observed to be useful as a feature extraction region due to their textural properties.
Keywords: Cyclotorsion; Eye registration; Feature extraction; Feature matching; Scleral blood vessel.
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