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Thickness Estimation with Optical Coherence Tomography and Statistical Decision Theory

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Abstract

We implement a maximum-likelihood (ML) estimator to interpret Optical Coherence Tomography (OCT) data, based on a Fourier-Domain OCT and a two-interface tear film model. We use the root mean square error as a figure of merit to quantify the system performance to estimate the tear film thickness. The impact of detector integration time is quantified. For an OCT system with a 1 μm axial resolution, the ML estimator can estimate up to 40 nm with a 10% relative error.

© 2013 Optical Society of America

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