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Multiple polynomial regression method for determination of biomedical optical properties from integrating sphere measurements

Dam, J. S ; Dalgaard, T ; Fabricius, P. E and Andersson-Engels, Stefan LU (2000) In Applied Optics 39(7). p.1202-1209
Abstract
We present a new, to our knowledge, method for extracting optical properties from integrating sphere measurements on thin biological samples. The method is based on multivariate calibration techniques involving Monte Carlo simulations, multiple polynomial regression, and a Newton-Raphson algorithm for solving nonlinear equation systems. Prediction tests with simulated data showed that the mean relative prediction error of the absorption and the reduced scattering coefficients within typical biological ranges were less than 0.3%. Similar teats with data from integrating sphere measurements on 20 dye-polystyrene microsphere phantoms led to mean errors less than 1.7% between predicted and theoretically calculated values. Comparisons showed... (More)
We present a new, to our knowledge, method for extracting optical properties from integrating sphere measurements on thin biological samples. The method is based on multivariate calibration techniques involving Monte Carlo simulations, multiple polynomial regression, and a Newton-Raphson algorithm for solving nonlinear equation systems. Prediction tests with simulated data showed that the mean relative prediction error of the absorption and the reduced scattering coefficients within typical biological ranges were less than 0.3%. Similar teats with data from integrating sphere measurements on 20 dye-polystyrene microsphere phantoms led to mean errors less than 1.7% between predicted and theoretically calculated values. Comparisons showed that our method was more robust and typically 5-10 times as fast and accurate as two other established methods, i.e., the inverse adding-doubling method and the Monte Carlo spline interpolation method. (C) 2000 Optical Society of America. (Less)
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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Applied Optics
volume
39
issue
7
pages
1202 - 1209
publisher
Optical Society of America
external identifiers
  • scopus:0001039928
ISSN
2155-3165
DOI
10.1364/AO.39.001202
language
English
LU publication?
yes
id
342afb1e-4e17-41f4-90ab-26e89aec8066 (old id 2257808)
date added to LUP
2016-04-04 08:55:53
date last changed
2022-01-29 07:50:25
@article{342afb1e-4e17-41f4-90ab-26e89aec8066,
  abstract     = {{We present a new, to our knowledge, method for extracting optical properties from integrating sphere measurements on thin biological samples. The method is based on multivariate calibration techniques involving Monte Carlo simulations, multiple polynomial regression, and a Newton-Raphson algorithm for solving nonlinear equation systems. Prediction tests with simulated data showed that the mean relative prediction error of the absorption and the reduced scattering coefficients within typical biological ranges were less than 0.3%. Similar teats with data from integrating sphere measurements on 20 dye-polystyrene microsphere phantoms led to mean errors less than 1.7% between predicted and theoretically calculated values. Comparisons showed that our method was more robust and typically 5-10 times as fast and accurate as two other established methods, i.e., the inverse adding-doubling method and the Monte Carlo spline interpolation method. (C) 2000 Optical Society of America.}},
  author       = {{Dam, J. S and Dalgaard, T and Fabricius, P. E and Andersson-Engels, Stefan}},
  issn         = {{2155-3165}},
  language     = {{eng}},
  number       = {{7}},
  pages        = {{1202--1209}},
  publisher    = {{Optical Society of America}},
  series       = {{Applied Optics}},
  title        = {{Multiple polynomial regression method for determination of biomedical optical properties from integrating sphere measurements}},
  url          = {{https://lup.lub.lu.se/search/files/5213188/2297126.pdf}},
  doi          = {{10.1364/AO.39.001202}},
  volume       = {{39}},
  year         = {{2000}},
}