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Updated: Feb 17, 2026

Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
Published on: July 5, 2016
Holography microscopy as an artifact-free alternative to phase-contrast
Lukáš Pastorek1,2, Tomáš Venit3, Pavel Hozák4,5,6
1Department of Biology of the Cell Nucleus, Institute of Molecular Genetics, Czech Academy of Sciences, Vídeňská 1083, 142 20, Prague 4, Czech Republic.
Abstract:
Artifact-free microscopic images represent a key requirement of multi-parametric image analysis in modern biomedical research. Holography microscopy (HM) is one of the quantitative phase imaging techniques, which has been finding new applications in life science, especially in morphological screening, cell migration, and cancer research. Rather than the classical imaging of absorbing (typically stained) specimens by bright-field microscopy, the information about the light-wave's phase shifts induced by the biological sample is employed for final image reconstruction. In this comparative study, we investigated the usability and the reported advantage of the holography imaging. The claimed halo-free imaging was analyzed compared to the widely used Zernike phase-contrast microscopy. The intensity and phase cross-membrane profiles at the periphery of the cell were quantified. The intensity profile for cells in the phase-contrast images suffers from the significant increase in intensity values around the cell border. On the contrary, no distorted profile is present outside the cell membrane in holography images. The gradual increase in phase shift values is present in the internal part of the cell body projection in holography image. This increase may be related to the increase in the cell internal material according to the dry mass theory. Our experimental data proved the halo-free nature of the holography imaging, which is an important prerequisite of the correct thresholding and cell segmentation, nowadays frequently required in high-content screening and other image-based analysis. Consequently, HM is a method of choice whenever the image analysis relies on the accurate data on cell boundaries.
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