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Application of genetic algorithms to an inverse problem of light propagation in tissues: reconstruction of the location and size of a tumor in a tissue volume

The light propagation in tissues has been simulated successfully by Monte Carlo method. Since the result of Monte Carlo simulation is discrete and statistic, the traditional optimizations and search algorithms encounter irresolvable difficulty in an inverse problem, but it can be overcome effectively by Genetic Algorithms due to their global search, robustness and self-adapting. In the present paper, we consider a tissue volume in which there is a spherical tumor with different optical parameters. A thin collimated NIR laser beam incidents normally on the surface. Monte Carlo method is used as a solution to the direct problem after some improvements, and in the inverse problem a genetic algorithm is applied to reconstruct the tumor. An appropriate evaluation function related to forward light distribution is designed to improve the stability and the speed of convergence. The selection based on fitness expectation is adopted as well as the random crossover and random mutation. Four parameters which determine the location and size of the tumor are to be reconstructed. The reconstructed tumor has a good agreement with the true tumor.