Thermogram Assessment for Tumor Parameter Estimation Considering Body Geometry

In this paper, an estimation methodology (inverse process) is presented to forecast unknown thermal and geometrical parameters of a tumor region using a temperature profile, which may be obtained by infrared thermography, on the skin surface of different organs. To solve the inverse problem, a bioheat transfer model with nonlinear boundary conditions was applied to rectangular, cylindrical, hemispherical, and deformed hemispherical body parts using finite-element analysis software. Then, the genetic algorithm (GA) was used to estimate the major parameters, such as depth, heat generation rate, and tumor size, by minimizing a fitness function involving the temperature profiles obtained from simulated or clinical data with those obtained from numerical simulation. The obtained results show consistency between the actual and forecasted parameters. It was determined that a GA-based methodology is well suited for the estimation problem, since the depth, heat generation rate, and size of the heat source have been accurately anticipated.