{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/efficient-quantitative-hyperspectral-image","title":"Efficient quantitative hyperspectral image unmixing method for large-scale Raman micro-spectroscopy data analysis","arxiv_id":"1803.01823","date":"2018-03-05","proceeding":null,"authors":["E. G. Lobanova","S. V. Lobanov"],"abstract":"Vibrational micro-spectroscopy is a powerful optical tool, providing a non-invasive label-free chemically specific imaging for many chemical and biomedical applications. However, hyperspectral image produced by Raman micro-spectroscopy typically consists of thousands discrete pixel points, each having individual Raman spectrum at thousand wavenumbers, and therefore requires appropriate image unmixing computational methods to retrieve non-negative spatial concentration and corresponding non-negative spectra of the image biochemical constituents. In this article, we present a new efficient Quantitative Hyperspectral Image Unmixing (Q-HIU) method for large-scale Raman micro-spectroscopy data analysis. This method enables to simultaneously analyse multi-set Raman hyperspectral images in three steps: (i) Singular Value Decomposition with innovative Automatic Divisive Correlation (SVD-ADC) which autonomously filters spatially and spectrally uncorrelated noise from data; (ii) a robust subtraction of fluorescent background from the data using a newly developed algorithm called Bottom Gaussian Fitting (BGF); (iii) an efficient Quantitative Unsupervised/Partially Supervised Non-negative Matrix Factorization method (Q-US/PS-NMF), which rigorously retrieves non-negative spatial concentration maps and spectral profiles of the samples' biochemical constituents with no a priori information and with great operation speed. Alternatively, the Q-US/PS-NMF is capable to work as a partially supervised method, when one or several samples' constituents are known, which significantly widens chemical and biomedical applications of the method. We apply the Q-HIU to the analysis of real large-scale Raman hyperspectral images of human atherosclerotic aortic tissues and our results show a proof-of-principle for the proposed method to retrieve the biochemical composition of the tissues.","url_abs":"http://arxiv.org/abs/1803.01823v3","url_pdf":"http://arxiv.org/pdf/1803.01823v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"efficient-quantitative-hyperspectral-image","repo_url":"https://github.com/LobanovaEG-LobanovSV/Q-HIU","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}