Microwave Tomography for Breast Cancer Detection Using Level Sets

Abstract: In this paper we analyze the potential of a shape-based model for the early detection of breast tumors from microwave data. The tumors are modeled using a level-set technique. The formulation as a shape-reconstruction problem offers several advantages compared to more traditional pixel-based schemes, to mention in particular well-defined boundaries and the incorporation of an intrinsic regularization that reduces the dimensionality of the inverse problem whereby at the same time stabilizing the reconstruction. We present in this paper a novel strategy that is able to detect very small tumors compared to the wavelength used for illuminating the breast. In addition, our algorithm is able to determine the sizes and the dielectric properties of the tumors with good accuracy. Numerical experiments are shown in 2D which demonstrate the performance of this new technique in realistic situations