Selecting optimal features from Fourier transform infrared spectroscopy for discrete-frequency imaging. Issue 5 (6th February 2018)

• Record Type: Journal Article
• Authors: Mankar, Rupali; Walsh, Michael J.; Bhargava, Rohit; Prasad, Saurabh; Mayerich, David
• Is Part Of: Analyst. Volume 143:Issue 5(2018)
• Page Start: 1147
• Page End: 1156
• Publication Date: 2018-02-06
• Subjects: Chemistry, Analytic -- Periodicals; 543
• DOI: 10.1039/c7an01888f
• Languages: English
• ISSNs: 0003-2654

Abstract

Abstract : Augmenting tissue histology with spectral information from mid-IR spectroscopy will benefit a lot. Optimal bands selection needed to do histopathological classification of mid-IR images will help to reduce imaging time and computation complexity. GPU use for feature selection makes it practical. 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.