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- W4205282170 abstract "Stimulated Raman scattering (SRS) is a high-speed optical imaging modality that can perform label-free metabolic imaging at the single-cell level. To resolve metabolites with overlapping Raman features, hyperspectral SRS (hsSRS) has been developed, which produces a three-dimensional image stack by measuring a series of SRS images over a range of Raman bands. However, a raw hsSRS image poorly reflects the information on the chemical composition and the abundance of each component. Thus, it is necessary to condense the information of hsSRS by converting it into chemical concentration (or segmentation) maps. In this chapter, we introduce major spectral unmixing tools to decipher chemical information from a hyperspectral image. Based on whether the prior knowledge of chemical composition is provided, we organized the methods into two categories, namely unsupervised and supervised. For unsupervised unmixing, one can apply dimension reduction and produce segmentation maps, or utilize nonnegative matrix factorization algorithms to generate both spectral profiles and concentration maps. For supervised unmixing, concentration maps can be generated through classical least-square fitting given the pure components. Alternatively, supervised unmixing can aim at training a classifier to label SRS images for clinical diagnosis." @default.
- W4205282170 created "2022-01-25" @default.
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- W4205282170 date "2022-01-01" @default.
- W4205282170 modified "2023-09-25" @default.
- W4205282170 title "Converting hyperspectral SRS into chemical maps" @default.
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- W4205282170 doi "https://doi.org/10.1016/b978-0-323-85158-9.00014-2" @default.
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