Presentation
19 June 2024 Correction for extrinsic background in Raman hyperspectral images: suppression of data leakage
Author Affiliations +
Abstract
Raman hyperspectral microscopy is a valuable tool in biological and biomedical imaging. Because Raman scattering is often weak in comparison to other phenomena, prevalent spectral fluctuations and contaminations have brought advancements in analytical and chemometric methods for Raman spectra. These chemometric advances have been key contributors to the applicability of Raman imaging to biological systems. As studies increase in scale, spectral contamination from extrinsic background, intensity from sources such as the optical components that are extrinsic to the sample of interest, has become an emerging issue. Although existing baseline correction schemes often reduce intrinsic background such as autofluorescence originating from the sample of interest, extrinsic background is not explicitly considered, and these methods often fail to reduce its effects. Here we show that extrinsic background can significantly affect a classification model using Raman images, yielding misleadingly high accuracies in the distinction of benign and malignant samples of follicular thyroid cell lines. To mitigate its effects, we develop extrinsic background correction (EBC) and demonstrate its use in combination with existing methods on Raman hyperspectral images. EBC isolates regions containing the smallest amounts of sample materials that retain extrinsic contributions that are specific to the device or environment. We perform classification both with and without the use of EBC, and we find that EBC retains biological characteristics in the spectra while significantly reducing extrinsic background. We also address its possible generalization for inhomogeneous illumination profile.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Tamiki Komatsuzaki "Correction for extrinsic background in Raman hyperspectral images: suppression of data leakage", Proc. SPIE PC13011, Data Science for Photonics and Biophotonics, PC1301101 (19 June 2024); https://doi.org/10.1117/12.3016551
Advertisement
Advertisement
KEYWORDS
Raman spectroscopy

Hyperspectral imaging

Biological samples

Biomedical optics

Chemometrics

Contamination

Imaging systems

Back to Top