ArticleBiomedical optics express2016
Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy.
Article in Biomedical optics express, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- OAH-Net: a deep neural network for efficient and robust hologram reconstruction for off-axis digital holographic microscopy.Biomedical optics express · 2025Article
- Article
- A Novel Approach for Measurement of Free-Form Optical Elements with Digital Holographic Microscopy.Micromachines · 2022Article
- Article
- Video-Rate Quantitative Phase Imaging Using a Digital Holographic Microscope and a Generative Adversarial Network.Sensors (Basel, Switzerland) · 2021Article
- Digital holographic microscopy evaluation of dynamic cell response to electroporation.Biomedical optics express · 2021Article
- Label-free multimodal quantitative imaging flow assay for intrathrombus formation in vitro.Biophysical journal · 2021Article
- Holo-UNet: hologram-to-hologram neural network restoration for high fidelity low light quantitative phase imaging of live cells.Biomedical optics express · 2020Article
- Adaptive frequency filtering based on convolutional neural networks in off-axis digital holographic microscopy.Biomedical optics express · 2019Article
- Automated red blood cells extraction from holographic images using fully convolutional neural networks.Biomedical optics express · 2017Article
- Changes in optical properties of electroporated cells as revealed by digital holographic microscopy.Biomedical optics express · 2017Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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 method 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 paves way to greater autonomy in automated DHM imaging for imaging live cell in thick cell cultures.
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