Maximum a posteriori estimator for high-contrast image composition of optical coherence tomography
A quantitative signal amplitude estimator for optical coherence tomography (OCT) is presented. It is based on a statistical model of OCT signal and noise, using a Bayesian maximum a posteriori (MAP) estimation framework. Multiple OCT images are used for estimation, similar to the widely utilized intensity averaging method. The estimator is less biased especially at low-intensity regions, where intensity averaging approaches the noise power and hence is biased. The estimator is applied to posterior ocular OCT images and provides high-contrast visualization of pathologies. In addition, histogram analysis objectively shows the superior performance of the estimator compared with intensity averaging.
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