Sparse localization of tumors inside an inhomogeneous breast

We discuss the application of the sparse processing techniques in the differential breast-cancer imaging. We derive the sparse model by assuming that the parameters of the inhomogeneous breast model are known from the previous measurements. In the linear model, we use the numerically computed three-dimensional Green's functions. We investigate the role of the regularization parameter and the number of sensors on the solution accuracy.