Anatomically realistic numerical breast models are essential tools for microwave breast imaging, supporting feasibility analysis, performance verification, and design improvements. Patient-specific models also assist in interpreting the results of the patient studies conducted on microwave imaging prototype systems. The proposed method employs automated and robust 3D processing techniques to construct flexible and reconfigurable breast models. These techniques include noise and artifact suppression with a principal component analysis (PCA) approach, and oversampling of the magnetic resonance imaging (MRI) data to enhance the intensity continuity. The k-means clustering segmentation identifies fatty and fibroglandular tissues and further segments these regions into a selected number of tissues, providing reconfigurable models. A peak Gaussian fitting technique maps the model clusters to the dielectric properties. The robustness of the proposed method is verified by applying it to both 1.5- and 3-T MRI scans as well as to scans of varying breast densities.
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