Correspondence recovery in 2-view mammography

Predicting breast tissue deformation is of great significance in various medical practice including diagnosis and surgery. In breast surgery, surgeons are often concerned with a specific portion of the breast, e.g., tumor, which must be located accurately. Clinically it is important to combine information provided by images from several modalities or at different times. However, images taken by various techniques are often obtained under entirely different tissue configurations, compression, orientation or body position. Hence, some form of spatial nonrigid transformation of image data is required so that the tissues are represented in an equivalent configuration. In this paper we propose finite element method (FEM) based strategy for correspondence identification between image features identified in two view mammography. The algorithm models breast compression during mammography and allows for correspondence recovery of 2D features found in two views and reconstruction of their 3D locations. The algorithm was tested on MRI and mammography images of triple modality phantom and limited set of mammographic images.