Literature DB >> 18238226

Reconstructing specimens using DIC microscope images.

F Kagalwala1, T Kanade.   

Abstract

Differential interference contrast (DIC) microscopy is a powerful visualization tool used to study live biological cells. Its use, however, has been limited to qualitative observations. The inherent nonlinear relationship between the object properties and the image intensity makes quantitative analysis difficult. Toward quantitatively measuring optical properties of objects from DIC images, we develop a method to reconstruct the specimen's optical properties over a three-dimensional (3-D) volume. The method is a nonlinear optimization which uses hierarchical representations of the specimen and data. As a necessary tool, we have developed and validated a computational model for the DIC image formation process. We test our algorithm by reconstructing the optical properties of known specimens.

Year:  2003        PMID: 18238226     DOI: 10.1109/TSMCB.2003.816924

Source DB:  PubMed          Journal:  IEEE Trans Syst Man Cybern B Cybern        ISSN: 1083-4419


  4 in total

1.  Orientation-independent differential interference contrast microscopy and its combination with an orientation-independent polarization system.

Authors:  Michael Shribak; James LaFountain; David Biggs; Shinya Inouè
Journal:  J Biomed Opt       Date:  2008 Jan-Feb       Impact factor: 3.170

2.  Using liquid crystal variable retarders for fast modulation of bias and shear direction in quantitative differential interference contrast (DIC) microscope.

Authors:  Michael Shribak
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2013-02-22

3.  CTRL - a label-free artificial intelligence method for dynamic measurement of single-cell volume.

Authors:  Kai Yao; Nash D Rochman; Sean X Sun
Journal:  J Cell Sci       Date:  2020-04-14       Impact factor: 5.285

4.  Physically-based in silico light sheet microscopy for visualizing fluorescent brain models.

Authors:  Marwan Abdellah; Ahmet Bilgili; Stefan Eilemann; Henry Markram; Felix Schürmann
Journal:  BMC Bioinformatics       Date:  2015-08-13       Impact factor: 3.169

  4 in total

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