Abstract
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.
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Acknowledgments
This work was supported by Mitacs and Campus France (Research Grant IT5501). The authors wish to thank Dr. Jérome Idier and Dr. Andrea Ridolfi for the practical implementation of the PRF-based segmentation algorithm and Dr. Stéphane Bedwani for its contribution of the realization of this work.
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Goubalan, S.R.T.J., Goussard, Y., Maaref, H. (2016). Markovian Approach to Automatic Annotation of Breast Mass Spicules Using an A Contrario Model. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_58
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