Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy

TL;DR

This paper introduces an automated Fourier space region-recognition filtering method for off-axis digital holographic microscopy, enhancing image quality and enabling fully automated, high-throughput, label-free cellular imaging.

Contribution

It presents a novel automated adaptive filtering technique combining shape recognition and iterative thresholding for improved Fourier space filtering in DHM.

Findings

Outperforms previous histogram thresholding methods in scattering media

Automates extraction of optical height differences in red blood cells

Enables autonomous imaging of live cells in thick cultures

Abstract

Automated label-free quantitative imaging of biological samples can greatly benefit high throughput diseases diagnosis. Digital holographic microscopy (DHM) is a powerful quantitative label-free imaging tool that retrieves structural details of cellular samples non-invasively. In off-axis DHM, a proper spatial filtering window in Fourier space is crucial to the quality of reconstructed phase image. Here we describe a region-recognition approach that combines shape recognition with an iterative thresholding to extracts the optimal shape of frequency components. The region recognition technique offers fully automated adaptive filtering that can operate with a variety of samples and imaging conditions. When imaging through optically scattering biological hydrogel matrix, the technique surpasses previous histogram thresholding techniques without requiring any manual intervention. Finally, we automate the extraction of the statistical difference of optical height between malaria parasite infected and uninfected red blood cells. The method described here pave way to greater autonomy in automated DHM imaging for imaging live cell in thick cell cultures.