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A multispectral Bayesian-based computational microscopy method for enhancing image quality
Author(s): Jason L. Deglint; Chao Jin; Alexander Wong
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Paper Abstract

Brightfield microscopy is a standard method for the identification and enumeration of different micro-organisms, specifically for analyzing different types of algae and planktonic organisms in water samples. Typically, bright- field microscopy is performed in a broadband visible spectrum configuration; however, important distinguishing features in various micro-organisms are much better captured using a narrow-band multispectral configuration. One challenge with leveraging multispectral microscopy, particularly in low-cost field-portable instrument setups, is the presence of significant chromatic aberrations. Therefore, we introduce a multispectral Bayesian-based computational microscopy method for enhancing image quality by jointly correcting for chromatic aberrations, illumination inhomogeneities and noise across multiple spectral wavelengths within a probabilistic framework. To test the efficacy of this method, calibration parameters associated with a field-portable multi-spectral mi- croscopy instrument are measured by characterizing the point spread functions at different spectral wavelengths ranging from 465 nm - 655 nm with a pinhole target. We demonstrate the effective optical resolution improvements of the microscopy instrument augmented with the proposed method using the 1951 USAF resolution test chart. Finally, we evaluate the qualitative performance of this instrument by imaging Anabaena flos-aqua, a toxin-producing cyanobacteria, as well as Ankistrodesmus falcatus, a type of green algae. The efficacy of this proposed framework shows the potential of having an in-situ instrument to observe biological organisms at mul- tiple narrow-band wavelengths, providing both additional spectral information and the ability for continuous detection and monitoring of micro-organisms.

Paper Details

Date Published: 4 March 2019
PDF: 11 pages
Proc. SPIE 10881, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVII, 1088117 (4 March 2019); doi: 10.1117/12.2506894
Show Author Affiliations
Jason L. Deglint, Univ. of Waterloo (Canada)
Waterloo Artificial Intelligence Institute (Canada)
Chao Jin, Univ. of Waterloo (Canada)
Sun Yat-sen Univ. (China)
Alexander Wong, Univ. of Waterloo (Canada)
Waterloo Artificial Intelligence Institute (Canada)


Published in SPIE Proceedings Vol. 10881:
Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVII
Daniel L. Farkas; Attila Tarnok; James F. Leary, Editor(s)

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