Optical property inversion of biological materials using Fourier series expansion and LS-SVM for hyperspectral imaging

Abstract

Determination of the optical properties of biological materials based on steady-state spatially resolved diffuse reflectance imaging is a complicated inverse problem-solving process. An effective inverse algorithm is first necessary and validated. This article proposed a Fourier series expansion (FSE) coupled with least squares support vector machine (LS-SVM) as an inverse algorithm for determining the absorption coefficient (μa) and the reduced scattering coefficient () of biological materials. A hyperspectral imaging system was used to acquire scattering images and steady-state spatially resolved diffuse reflectance profiles of liquid phantoms. Experiment results demonstrated that the hyperspectral imaging system coupled with this inverse algorithm effectively improved prediction accuracy of both μa and of liquid phantoms. Tests on liquid phantoms showed that the mean relative errors of this inverse algorithm to be 11.03% for the absorption coefficient and 7.16% for the reduced scattering coefficient when corresponding Fourier coefficients of liquid phantoms were used to develop the prediction model. To further study the method, the Fourier coefficients calculated from normalized Monte Carlo simulation data were used to develop the prediction model for determining the optical properties of 36 liquid phantoms. The prediction errors were 15.96 and 10.91% for μa and , respectively. For all liquid phantoms, it was found that the prediction values of both μa and were generally in good agreement with their actual values. Therefore, the FSE–LS-SVM method provides an effectively means for improving the prediction accuracy of optical properties of biological materials.