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2 March 2015 Estimating skin blood saturation by selecting a subset of hyperspectral imaging data
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Abstract
Skin blood haemoglobin saturation (𝑠b) can be estimated with hyperspectral imaging using the wavelength (λ) range of 450-700 nm where haemoglobin absorption displays distinct spectral characteristics. Depending on the image size and photon transport algorithm, computations may be demanding. Therefore, this work aims to evaluate subsets with a reduced number of wavelengths for 𝑠b estimation. White Monte Carlo simulations are performed using a two-layered tissue model with discrete values for epidermal thickness (𝑇epi) and the reduced scattering coefficient (μ's ), mimicking an imaging setup. A detected intensity look-up table is calculated for a range of model parameter values relevant to human skin, adding absorption effects in the post-processing. Skin model parameters, including absorbers, are; μ's (λ), 𝑇epi, haemoglobin saturation (𝑠b), tissue fraction blood (𝑐b) and tissue fraction melanin (𝑐mel). The skin model paired with the look-up table allow spectra to be calculated swiftly. Three inverse models with varying number of free parameters are evaluated: A(𝑠b, 𝑐b), B(𝑠b, 𝑐b, 𝑐mel) and C(all parameters free). Fourteen wavelength candidates are selected by analysing the maximal spectral sensitivity to 𝑠b and minimizing the sensitivity to 𝑐b. All possible combinations of these candidates with three, four and 14 wavelengths, as well as the full spectral range, are evaluated for estimating 𝑠b for 1000 randomly generated evaluation spectra. The results show that the simplified models A and B estimated 𝑠b accurately using four wavelengths (mean error 2.2% for model B). If the number of wavelengths increased, the model complexity needed to be increased to avoid poor estimations.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Maria Ewerlöf, E. Göran Salerud, Tomas Strömberg, and Marcus Larsson "Estimating skin blood saturation by selecting a subset of hyperspectral imaging data", Proc. SPIE 9328, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIII, 93280J (2 March 2015); https://doi.org/10.1117/12.2075292
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