Issue 5, 2018

Selecting optimal features from Fourier transform infrared spectroscopy for discrete-frequency imaging

Abstract

Tissue histology utilizing chemical and immunohistochemical labels plays an important role in biomedicine and disease diagnosis. Recent research suggests that mid-infrared (IR) spectroscopic imaging may augment histology by providing quantitative molecular information. One of the major barriers to this approach is long acquisition time using Fourier-transform infrared (FTIR) spectroscopy. Recent advances in discrete frequency sources, particularly quantum cascade lasers (QCLs), may mitigate this problem by allowing selective sampling of the absorption spectrum. However, DFIR imaging only provides a significant advantage when the number of spectral samples is minimized, requiring a priori knowledge of important spectral features. In this paper, we demonstrate the use of a GPU-based genetic algorithm (GA) using linear discriminant analysis (LDA) for DFIR feature selection. Our proposed method relies on pre-acquired broadband FTIR images for feature selection. Based on user-selected criteria for classification accuracy, our algorithm provides a minimal set of features that can be used with DFIR in a time-frame more practical for clinical diagnosis.

Graphical abstract: Selecting optimal features from Fourier transform infrared spectroscopy for discrete-frequency imaging

Article information

Article type
Paper
Submitted
21 nov 2017
Accepted
15 jan 2018
First published
16 jan 2018

Analyst, 2018,143, 1147-1156

Selecting optimal features from Fourier transform infrared spectroscopy for discrete-frequency imaging

R. Mankar, M. J. Walsh, R. Bhargava, S. Prasad and D. Mayerich, Analyst, 2018, 143, 1147 DOI: 10.1039/C7AN01888F

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