Spectral library and method for sparse unmixing of hyperspectral images in fluorescence guided resection of brain tumors.

ABSTRACT: Through spectral unmixing, hyperspectral imaging (HSI) in fluorescence-guided brain tumor surgery has enabled the detection and classification of tumor regions invisible to the human eye. Prior unmixing work has focused on determining a minimal set of viable fluorophore spectra known to be present in the brain and effectively reconstructing human data without overfitting. With these endmembers, non-negative least squares regression (NNLS) was commonly used to compute the abundances. However, HSI images are heterogeneous, so one small set of endmember spectra may not fit all pixels well. Additionally, NNLS is the maximum likelihood estimator only if the measurement is normally distributed, and it does not enforce sparsity, which leads to overfitting and unphysical results. In this paper, we

SUBMITTER: Black D 

PROVIDER: S-EPMC11427211 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature