Hyperspectral image analysis for CARS, SRS, and Raman data

Masia, Francesco ORCID: https://orcid.org/0000-0003-4958-410X, Karuna, Arnica, Borri, Paola ORCID: https://orcid.org/0000-0002-7873-3314 and Langbein, Wolfgang Werner ORCID: https://orcid.org/0000-0001-9786-1023 2015. Hyperspectral image analysis for CARS, SRS, and Raman data. Journal of Raman Spectroscopy 46 (8) , pp. 727-734. 10.1002/jrs.4729

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Abstract

In this work, we have significantly enhanced the capabilities of the hyperspectral image analysis (HIA) first developed by Masia et al. [1] The HIA introduced a method to factorize the hyperspectral data into the product of component concentrations and spectra for quantitative analysis of the chemical composition of the sample. The enhancements shown here comprise (1) a spatial weighting to reduce the spatial variation of the spectral error, which improves the retrieval of the chemical components with significant local but small global concentrations; (2) a new selection criterion for the spectra used when applying sparse sampling[2] to speed up sequential hyperspectral imaging; and (3) a filter for outliers in the data using singular value decomposition, suited e.g. to suppress motion artifacts. We demonstrate the enhancements on coherent anti-Stokes Raman scattering, stimulated Raman scattering, and spontaneous Raman data. We provide the HIA software as executable for public use.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Biosciences Physics and Astronomy
Subjects:	Q Science > QC Physics
Uncontrolled Keywords:	coherent Raman micro-spectroscopy; hyperspectral image analysis; sparse sampling
Publisher:	Wiley-Blackwell
ISSN:	0377-0486
Funders:	EPSRC, BBSRC
Date of First Compliant Deposit:	30 March 2016
Date of Acceptance:	5 May 2015
Last Modified:	09 May 2023 00:25
URI:	https://orca.cardiff.ac.uk/id/eprint/74543

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