A Spectral Library and Method for Sparse Unmixing of Hyperspectral Images in Fluorescence Guided Resection of Brain Tumors

David Black1*, Benoit Liquet2,3,4, Sadahiro Kaneko5, Antonio Di leva4,6, Walter Stummer7, Eric Suero Molina4,6,7*

1 Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.

2 School of Mathematical and Physical Sciences, Macquarie University, Sydney, Australia

3 Laboratoire de Mathématiques et de ses Applications, E2S-UPPA, Université de Pau & Pays de L’Adour, France

4 Computational NeuroSurgery (CNS) Lab, Macquarie University, Sydney, New South Wales, Australia

5 Department of Neurosurgery, Hokkaido Medical Center, Hokkaido, Japan

6 Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia

7 Department of Neurosurgery, University Hospital of Münster, Münster, Germany

Code: Includes MATLAB code for unmixing and generation of simulated spectra
Endmembers.csv: The endmember spectra (columns 2 to 10) and the corresponding wavelengths in nm (column 1)
Unmixing_Paper.pdf: The paper, also available at the link below

Please consider citing if useful: David Black, Benoit Liquet, Sadahiro Kaneko, Antonio Di leva, Walter Stummer, Eric Suero Molina. A Spectral Library and Method for Sparse Unmixing of Hyperspectral Images in Fluorescence Guided Resection of Brain Tumors. Biomedical Optics Express, 15(8), pp. 4406-4424, 2024. DOI: 10.1364/BOE.528535