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

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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

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Publication details

The article was received on 21 Nov 2017, accepted on 15 Jan 2018 and first published on 16 Jan 2018


Article type: Paper
DOI: 10.1039/C7AN01888F
Citation: Analyst, 2018, Advance Article
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    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, Advance Article , DOI: 10.1039/C7AN01888F

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