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A robust method for automated background subtraction of tissue fluorescence
Author(s) -
Cao Alex,
Pandya Abhilash K.,
Serhatkulu Gulay K.,
Weber Rachel E.,
Dai Houbei,
Thakur Jagdish S.,
Naik Vaman M.,
Naik Ratna,
Auner Gregory W.,
Rabah Raja,
Freeman D. Carl
Publication year - 2007
Publication title -
journal of raman spectroscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.748
H-Index - 110
eISSN - 1097-4555
pISSN - 0377-0486
DOI - 10.1002/jrs.1753
Subject(s) - raman spectroscopy , background subtraction , raman scattering , residual , polynomial , fluorescence , subtraction , noise (video) , ranging , minimax , computer science , imaging phantom , signal to noise ratio (imaging) , biological system , analytical chemistry (journal) , artificial intelligence , mathematics , algorithm , physics , optics , chemistry , mathematical optimization , mathematical analysis , chromatography , telecommunications , pixel , arithmetic , image (mathematics) , biology
Abstract This paper introduces a new robust method for the removal of background tissue fluorescence from Raman spectra. Raman spectra consist of noise, fluorescence and Raman scattering. In order to extract the Raman scattering, both noise and background fluorescence must be removed, ideally without human intervention and preserving the original data. We describe the rationale behind our robust background subtraction method, determine the parameters of the method and validate it using a Raman phantom against other methods currently used. We also statistically compare the methods using the residual mean square (RMS) with a fluorescence‐to‐signal (F/S) ratio ranging from 0.1 to 1000. The method, ‘adaptive minmax’, chooses the subtraction method based on the F/S ratio. It uses multiple fits of different orders to maximize each polynomial fit. The results show that the adaptive minmax method was significantly better than any single polynomial fit across all F/S ratios. This method can be implemented as part of a modular automated real‐time diagnostic in vivo Raman system. Copyright © 2007 John Wiley & Sons, Ltd.