Abstract
We have been developing an automated detection scheme for clustered microcalcifications on digital mammograms and reported the methods in several papers [1]–[4]. These schemes show a good performance in detection, but there is a problem that many false-positive candidates (ten and more) appear in some specific images. Therefore, an improvement of the elimination step of false positives is required. To achieve this, we have developed new methods of discrimination of the candidates and elimination of the false positives.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fujita H, Endo T, Matsubara T, et al. (1995) Automated detection of masses and clustered microcalcifications on mammograms. Proc. SPIE 2434, pp 682–692.
Hara T, Hirako K, Fujita H, et al. (1996) Automated detection algorithm for clustered microcalcifications based on density gradient and triplering filter analysis. In: K Doi et al. (eds.), Digital Mammography’ 96, Elsevier Science, Amsterdam, pp 257–262.
Hirako K, Fujita H, Hara T, et al. (1996) Development of detection filter for mammographic microcalcifications: A method based on density gradient and triplering filter analysis. Systems and Computers in Japan 27(13), pp 36–48.
Norhayati I, Fujita H, Hara T, et al. (1997) Automated detection of clustered microcalcifications on mammograms: CAD system application to MIAS database. Phys. Med. Biol. 42, pp 2577–2589.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Fukuoka, D. et al. (1998). Automated Detection of Clustered Microcalcifications on Digitized Mammograms. 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_31
Download citation
DOI: https://doi.org/10.1007/978-94-011-5318-8_31
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6234-3
Online ISBN: 978-94-011-5318-8
eBook Packages: Springer Book Archive