Unsupervised and Supervised Algorithms for Identification of Sample Pixels in FTIR Images

TL;DR

This paper introduces three algorithms, both unsupervised and supervised, for accurately identifying sample pixels in FTIR images, enhancing chemical mapping and analysis.

Contribution

It presents novel algorithms for sample pixel detection in FTIR images, improving accuracy and enabling automatic detection, advancing FTIR signal processing.

Findings

Algorithms demonstrate accurate sample and background pixel prediction.

Supervised method enables automatic detection of sample pixels.

Solutions are robust and contribute to FTIR image analysis.

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.