Polynomial Basis Functions for Qualitative Head Tissue Segmentation via Linearized Microwave Imaging

Microwave imaging of head tissue is a complex task due to the nonlinearity of the underlying inverse scattering problem. Such a difficulty can be alleviated by providing a convenient starting guess to inversion algorithms. To this end, we present an approach to provide a qualitative segmentation of the head tissue, i.e., an image able to identify the different tissue boundaries but not their electromagnetic properties. The proposed approach uses three-dimensional polynomial basis functions for the permittivity distribution under the first-order Born approximation and generalized Tikhonov regularization. To illustrate the possibilities of the proposed method, we apply it to the problem of permittivity reconstruction of the realistic head phantom comprising five tissues. Preliminary results show that the boundaries of all five tissues are identifiable.