Detection of small tumors in microwave medical imaging using level sets and music

We focus on the application of microwaves for the early detection of breast cancer. We investigate the potential of a novel strategy using shapes for modeling the tumor in the breast. An inversion using a shape-based model offers several advantages like 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 explore novel level-set techniques as a means to detect the tumor without any initialization of its position and size. We present some numerical resonstructions and we compare them with the conventional MUSIC algorithm, in particular with respect to the frequency which is used for the investigation. We show that for different frequencies these two methods show a different qualitative behaviour in the reconstructions.