Signal processing methods for high resolution microwave image reconstruction

Microwave image reconstruction is a highly complicated problem. Although it can potentially have a lot of applications in different areas, the development of efficient inverse scattering algorithms is one of the critical issues that have so far prevented the realization of practical and commercial microwave imaging apparatus where high resolution is desired e.g. in biomedical applications. The focus of this thesis is to show how signal and image processing algorithms can be utilized in the image reconstruction algorithms to provide additional information for improving the image resolution.;Regularization methods are used to remove the ill-posed answers in microwave image reconstruction problems. Traditional regularization methods are usually problem-independent and do not take advantage of a priori information specific to any particular imaging application. In this thesis, a novel problem-dependent regularization approach is introduced for the application of breast imaging. A real genetic algorithm (RGA) minimizes a cost functional that is the error between the recorded and simulated data. At each iteration of the RGA, based on a priori information about the shape of the breast profiles, a neural network classifier rejects the solutions that cannot be a map of the dielectric properties of a breast profile. The algorithm was tested against four realistic numerical breast phantoms including a mostly fatty, a scattered fibra-glandular, a hetereogenously dense and a very dense sample. The tests were also repeated where a 4mm x 4mm tumor was inserted in the fibra-glandular tissue in each of the four breast types. The results show the effectiveness of the proposed approach which to the best of our knowledge, has the highest resolution amongst the evolutionary algorithms used for the inversion of realistic numerical breast phantoms.