A level set evolution strategy in microwave imaging for early breast cancer detection

In this paper we propose and analyse a novel shape-reconstruction technique for the early detection of breast cancer from microwave data, which is based on a level-set technique. The shape-based approach offers several advantages compared to more traditional pixel-based approaches, as, for example, well-defined boundaries and the incorporation of an intrinsic regularization (in form of a-priori assumptions regarding the general anatomical structures present in the medium) that reduces the dimensionality of the inverse problem and thereby stabilizing the reconstruction. The level set strategy (which is an implicit representation of the shapes) frees us from topological restrictions during this reconstruction process. We present numerical results in 2D which demonstrate the performance of our scheme in various simulated realistic situations.