Bayesian estimation of tumours in breasts using microwave imaging

According to Canadian Cancer Society, breast cancer is the most frequently di- agnosed cancer in women. Our ability to potentially detect breast cancer in an early stage has potential of significantly decreasing mortality and hence is a very important issue in healthcare. Currently, mammography has been used widely for screening women over 50 who are statistically more vulnerable. However it suffers from some limitations such as false negative and positive results, using ionizing radiation and patient's discomfort. Microwave imaging has been intro- duced recently to overcome drawbacks of this method. In breast microwave imaging the imaging system consists of an array of antennas which can serve both as transmitting and receiving an- tennas. Therefore it is possible to illuminate object of interest (breast) from multiple directions thus obtaining a full three dimensional scan whose resolution depends only on the number of antennas. One of the most difficult parts when detecting potential tumours in breasts is presence of modelling noise due to large amount of scattering, which severely deteriorates performance of estimation and detection algorithms. In this paper, we propose a parametric 3D model of breast microwave propagation, i.e., signal measured on antennas with respect to tumours' and breast parameters including electromagnetic properties and geometry (our model includes multiple tu- mours with arbitrary shapes.) We illustrate the applicability of our results through numerical examples.