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Proceedings Paper

Multivariate data reduction techniques for hyperspectral Raman imaging
Author(s): Michael D. Morris; Jerilyn A. Timlin; Angela Carden; Catherine Perso Tarnowski; Clare M. Edwards
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Paper Abstract

Underlying the contrast in a hyperspectral Raman image are complete Raman spectra at each of tens or hundreds of thousands of pixels. Multivariate statistics allows reduction of these large data sets to manageable numbers of chemically significant descriptors that become the image contrast. In most cases an object can be viewed as containing a small number (usually fewer than ten) chemically discrete components, each with its own vibrational spectrum. Principal component analysis (PCA) and exploratory factor analysis (FA) can be used to generate descriptors from the experimentally observed Raman spectra in image data sets. Additionally, PCA and FA can be viewed as optimized weighted signal averaging techniques. FA contrast is generated from all regions of a spectrum that are attributable to one component. The result is better signal/noise ratio than is obtained using the height or area of a single band as image contrast. We will discuss a variety of preprocessing steps such as removing outliers and selecting spectral subregions for data analysis optimization. We will illustrate these concepts using an image of bone tissue.

Paper Details

Date Published: 14 March 2000
PDF: 8 pages
Proc. SPIE 3920, Spectral Imaging: Instrumentation, Applications, and Analysis, (14 March 2000); doi: 10.1117/12.379594
Show Author Affiliations
Michael D. Morris, Univ. of Michigan (United States)
Jerilyn A. Timlin, Univ. of Michigan (United States)
Angela Carden, Univ. of Michigan (United States)
Catherine Perso Tarnowski, Univ. of Michigan (United States)
Clare M. Edwards, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 3920:
Spectral Imaging: Instrumentation, Applications, and Analysis
Gregory H. Bearman; Dario Cabib; Richard M. Levenson, Editor(s)

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