Application of Information Entropy Based Evaluation Factor in the Analysis of Measured Data of Microwave Biomedical Imaging

In the reconstruction of complex permittivity, huge amounts of data in scattering fields measured around the body consume enormous calculations. In fact, for reconstruction, the contribution of data measured in different areas is quite dissimilar to the reconstruction. In this paper, we introduce the computational method of evaluation factor based on information entropy and Bayesian theory, to analyze the influence of data measured in different areas on the imaging quality before the reconstruction. By comparing the evaluation factor and reconstruction error, only the data measured in crucial areas are needed to reconstruct the permittivity, which can significantly reduce the computing time. In a 2-D microwave biomedical Imaging, we have verified the feasibility of this method.