A multispectral Bayesian-based computational microscopy method for enhancing image quality

Jason L. Deglint; Chao Jin; Alexander Wong

doi:10.1117/12.2506894

4 March 2019 A multispectral Bayesian-based computational microscopy method for enhancing image quality

Jason L. Deglint, Chao Jin, Alexander Wong

Proceedings Volume 10881, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVII; 1088117 (2019) https://doi.org/10.1117/12.2506894
Event: SPIE BiOS, 2019, San Francisco, California, United States

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.

Citation Download Citation

Jason L. Deglint, Chao Jin, and Alexander Wong "A multispectral Bayesian-based computational microscopy method for enhancing image quality", Proc. SPIE 10881, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVII, 1088117 (4 March 2019); https://doi.org/10.1117/12.2506894

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KEYWORDS

Image enhancement

Microscopy

Point spread functions

Image quality

Water

Chromatic aberrations

Image processing

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