Markovian Approach to Automatic Annotation of Breast Mass Spicules Using an A Contrario Model

In this paper, we propose a new method for automatic extraction of breast mass spicules in 2-D mammography. Spicules are abnormal curvilinear structures which characterize most of malignant breast masses. They are important features for discrimination between benign and malignant masses. In our method, the curvilinear structures are first approximated by line segments derived from localized Radon transforms; then, the Markov random field is used to take into account the local interactions via the contextual information between these segments. Finally, detection of the curvilinear structures that most likely correspond to spicules is performed using an a contrario framework. Validation of the approach was performed on a large dataset of spiculated masses which were selected from a public digital database; the results showed a high agreement with manually annotated mammograms.