Unsupervised and Supervised Methodologies for Identification of Sample Pixels in Fourier Transform InfraRed Microspectroscopic Images

Abstract

Mid-InfraRed spectroscopy is a promising label-free technique that can offer insights into morphological and pathological alterations in biological tissues at the molecular level. Owing to the development of the Fourier Transform InfraRed (FTIR) spectrometer, combined with scanning microspectroscopy, FTIR microspectroscopic images can be acquired by measuring spectral data from multiple spatial points, generating comprehensive chemical maps. In the data pre-processing, the identification of the sample pixels, with the background pixels excluded, is important for further effective feature extraction in FTIR images. Here, we present and compare three methodologies realized in unsupervised and supervised approaches for the identification of the sample pixels, and validate them in our experimentally tested FTIR images on tissue sections from multiple organs. The algorithms demonstrate accurate prediction results of the sample and background pixels, and the supervised method further enables the automatic detection. These findings highlight thorough and robust solutions to the sample pixels detection problem in FTIR images, contributing to the FTIR signal processing, and future chemical and clinical applications of FTIR images.