Robust object characterization from lensless microscopy videos

Lensless microscopy, also known as in-line digital holography, is a 3D quantitative imaging method used in various fields including microfluidics and biomedical imaging. To estimate the size and 3D location of microscopic objects in holograms, maximum likelihood methods have been shown to outperform traditional approaches based on 3D image reconstruction followed by 3D image analysis. However, the presence of objects other than the object of interest may bias maximum likelihood estimates. Using experimental videos of holograms, we show that replacing the maximum likelihood with a robust estimation procedure reduces this bias. We propose a criterion based on the intersection of confidence intervals in order to automatically set the level that distinguishes between inliers and outliers. We show that this criterion achieves a bias / variance trade-off. We also show that joint analysis of a sequence of holograms using the robust procedure is shown to further improve estimation accuracy.