Identifying chromophore fingerprints of brain tumor tissue on hyperspectral imaging using principal component analysis

TL;DR

This paper uses principal component analysis on hyperspectral imaging data to identify chromophore fingerprints in brain tumor tissues, aiding automated molecular profiling and biomarker discovery.

Contribution

It introduces a statistical analysis method linking spectral features to tissue chromophores, specifically cytochrome, in hyperspectral brain tumor imaging.

Findings

Spectral features correlate with cytochrome absorption spectra.

PCA identifies key spectral features for tissue differentiation.

Potential for automated biomarker discovery in brain tumors.

Abstract

Hyperspectral imaging (HSI) is an optical technique that processes the electromagnetic spectrum at a multitude of monochromatic, adjacent frequency bands. The wide-bandwidth spectral signature of a target object's reflectance allows fingerprinting its physical, biochemical, and physiological properties. HSI has been applied for various applications, such as remote sensing and biological tissue analysis. Recently, HSI was also used to differentiate between healthy and pathological tissue under operative conditions in a surgery room on patients diagnosed with brain tumors. In this article, we perform a statistical analysis of the brain tumor patients' HSI scans from the HELICoiD dataset with the aim of identifying the correlation between reflectance spectra and absorption spectra of tissue chromophores. By using the principal component analysis (PCA), we determine the most relevant spectral features for intra- and inter-tissue class differentiation. Furthermore, we demonstrate that such spectral features are correlated with the spectra of cytochrome, i.e., the chromophore highly involved in (hyper) metabolic processes. Identifying such fingerprints of chromophores in reflectance spectra is a key step for automated molecular profiling and, eventually, expert-free biomarker discovery.