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Proceedings Paper

Low bandwidth eye tracker for scanning laser ophthalmoscopy
Author(s): Zachary G. Harvey; Alfredo Dubra; Nathan D. Cahill; Sonia Lopez Alarcon
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Paper Abstract

The incorporation of adaptive optics to scanning ophthalmoscopes (AOSOs) has allowed for in vivo, noninvasive imaging of the human rod and cone photoreceptor mosaics. Light safety restrictions and power limitations of the current low-coherence light sources available for imaging result in each individual raw image having a low signal to noise ratio (SNR). To date, the only approach used to increase the SNR has been to collect large number of raw images (N >50), to register them to remove the distortions due to involuntary eye motion, and then to average them. The large amplitude of involuntary eye motion with respect to the AOSO field of view (FOV) dictates that an even larger number of images need to be collected at each retinal location to ensure adequate SNR over the feature of interest. Compensating for eye motion during image acquisition to keep the feature of interest within the FOV could reduce the number of raw frames required per retinal feature, therefore significantly reduce the imaging time, storage requirements, post-processing times and, more importantly, subject's exposure to light. In this paper, we present a particular implementation of an AOSO, termed the adaptive optics scanning light ophthalmoscope (AOSLO) equipped with a simple eye tracking system capable of compensating for eye drift by estimating the eye motion from the raw frames and by using a tip-tilt mirror to compensate for it in a closed-loop. Multiple control strategies were evaluated to minimize the image distortion introduced by the tracker itself. Also, linear, quadratic and Kalman filter motion prediction algorithms were implemented and tested and tested using both simulated motion (sinusoidal motion with varying frequencies) and human subjects. The residual displacement of the retinal features was used to compare the performance of the different correction strategies and prediction methods.

Paper Details

Date Published: 24 February 2012
PDF: 9 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831450 (24 February 2012); doi: 10.1117/12.911661
Show Author Affiliations
Zachary G. Harvey, Flaum Eye Institute, Univ. of Rochester (United States)
Rochester Institute of Technology (United States)
Alfredo Dubra, Medical College of Wisconsin (United States)
Univ. of Rochester (United States)
Nathan D. Cahill, Rochester Institute of Technology (United States)
Sonia Lopez Alarcon, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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