Solving Optical Skin Simulation Model Parameters Using Genetic Algorithm

Abstract

Near infrared spectroscopy is noninvasive method to obtain information from materials, such as human skin. A simulation model of light interaction with skin is used to simulate skin reflectance spectra when the chemical and physical parameters of the skin are known. Genetic algorithm is utilised for tuning the simulator to solve the inverse problem; to calculate the skin parameters from the measured reflectance spectra. The inverse problems are often ill-posed, which was also true for this problem in its original form. After assuming all physical parameters as fixed, the problem was regularised and a unique solution for blood melanin and water concentrations was found in all simulations. The accuracy and the uniqueness of the solution proved to be almost independent of the provided spectral resolution, as long as it is larger than three wavelengths. The accuracy of the solution depends on the MCML simulation noise level and the fitness function used. The performance of four different fitness functions was evaluated using fitness landscape and noise analysis, and the best of them was chosen. The achieved accuracy is satisfactory for many applications and it can probably be further improved by increasing the number of photons used in the MCML simulation or by further optimising the fitness function.