Abstract
Breast carcinoma is the main cause of deaths in women suffering from cancer and its early detection is vital in order to improve its prognosis [1]. Screen film mammography represents the method of choice and the only accepted screening modality. Clustered microcalcifications (mc) are one of the mammographic hallmarks of early breast cancer [2]. The detection and differentiation of cancer related mc’s from those occurring in benign processes are highly dependent on image quality as well as the skills and the vigilance of the reporting radiologist [3],[4],[5]. In clinical routine additional investigations (eccentric resp. magnified ray views, ultrasound, MRI) have to be performed in patients with inclusive findings on standard mammographic projections. Patients with findings suspicious have to undergo biopsy followed by histologic examination of the specimen.
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© 1998 Springer Science+Business Media Dordrecht
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Sorantin, E. et al. (1998). Automated Detection and Classification of Micro-Calcifications in Mammograms Using Artifical Neural Nets. In: Karssemeijer, N., Thijssen, M., Hendriks, J., van Erning, L. (eds) Digital Mammography. Computational Imaging and Vision, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5318-8_37
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DOI: https://doi.org/10.1007/978-94-011-5318-8_37
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