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A study is conducted to investigate the use of a parametric intensity model for the process of image classification in biomedical microwave tomography (MWT). This process allows for extracting structural information about an object-of-interest (OI), which can be incorporated as prior information in an inversion algorithm. The parametric intensity model is based on a supervised Gaussian probabilistic model. The generated intensity model is used to classify three cross-sectional MWT images of human lower leg models. The classification is based on a Bayesian decision classifier. The resulting segments are used to extract structural information about the legs’ contour.

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Conference paper

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