Estimating skin blood saturation by selecting a subset of hyperspectral imaging data

Skin blood haemoglobin saturation (s(b)) can be estimated with hyperspectral imaging using the wavelength (lambda) 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 s(b) estimation. White Monte Carlo simulations are performed using a two-layered tissue model with discrete values for epidermal thickness (T-epi) and the reduced scattering coefficient (mu 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 mu(s)' (lambda) T-epi, haemoglobin saturation (s(b)), tissue fraction blood (c(b)) and tissue fraction melanin (c(mel)). 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(s(b), c(b)), B(s(b), c(b), c(mel)) and C(all parameters free). Fourteen wavelength candidates are selected by analysing the maximal spectral sensitivity to s(b) and minimizing the sensitivity to s(b). All possible combinations of these candidates with three, four and 14 wavelengths, as well as the full spectral range, are evaluated for estimating s(b) for 1000 randomly generated evaluation spectra. The results show that the simplified models A and B estimated.. b 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.