Mammographic image segmentation using a tissue-mixture model and Markov random fields

The introduction of imaging and anatomical models can be used to develop robust algorithms for mammographic image analysis. The key to the proposed technique is to recognise that at any site in the observed image, a combination of tissues is present. The relationship between the different tissues is represented by a statistical model which is dictated by the imaging system. Markov random fields are used to model the anatomical knowledge. The two models are combined into a Bayesian framework to segment the image and extract regions of interest. Results indicate that reliable and verifiable analysis techniques can be developed utilising physically justified models. Representing the mammographic images in the proposed framework is intended to be more suitable for interpretation by human specialists.