Unsupervised and Supervised Algorithms for Identification of Sample Pixels in FTIR 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 devices, FTIR images can be produced by simultaneously acquiring 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 three algorithms realized in unsupervised and supervised approaches for the identification of the sample pixels. 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 research with FTIR images.