Abstract
A novel method for accurate detection of regions of interest (ROIs) that contain circumscribed lesions in X-rays mammograms based on bilateral subtraction is presented. Implementing this method requires left and right breast images alignment using a cross-correlation criterion followed by a windowing analysis in mammogram pairs. Furthermore, a set of qualification criteria is employed to filter these regions, retaining the most suspicious for which a Radial-Basis Function Neural Network makes the final decision marking them as ROIs that contain abnormal tissue. Extensive experiments have shown that the proposed method detects the location of the circumscribed lesions with accuracy of 95.8% in the MIAS database.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Martin, J., Moskowitz, M., Milbrath, J.: Breast cancer missed by mammography. AJR 132, 737 (1979)
Kalisher, L.: Factors influencing false negative rates in xero-mammography. Radiology 133, 297 (1979)
Tabar, L., Dean, B.P.: Teaching Atlas of Mammography, 2nd edn. Thieme, NY (1985)
Christoyianni, I., Dermatas, E., Kokkinakis, G.: Fast Detection of Masses in Computer- Aided Mammography. IEEE Signal Processing Magazine 17(1), 54–64 (2000)
Chan, H., Wei, D., Helvie, M., Sahiner, B., Adler, D., Goodsitt, M., Petrick, N.: Computer- Aided Classification of Mammographic Masses and Normal Tissue: Linear Discriminant analysis in Texture Feature Space. Phys. Med. Biol, 40, 857–876 (1995)
Meersman, D., Scheunders, P., Dyck, V.D.: Detection of Microcalcifications using Neural Networks. In: Proc. of the 3rd Int. Workshop on Digital Mammograph, Chicago, IL, pp. 97–103 (1996)
Dhawan, P.A., Chite, Y., Bonasso, C., Wheeler, K.: Radial-Basis-Function Based Classification of Mammographic Microcalcifications Using Texture Features. IEEE Engineering in Medicine and Biology & CMBEC, 535–536 (1995)
Sonka, M., Fitzpatrick, J.: Handbook of Medical Imaging. SPIE Press, San Jose (2000)
Doi, K., Giger, M., Nishikawa, R., Schmidt, R. (eds.): Digital Mammography 1996. Elsevier, Amsterdam (1996)
Méndez, A.J., Tahoces, P.G., Lado, M.J., Souto, M., Vidal, J.J.: Computer-aided diagnosis: Automatic detection of malignant masses in digitized mammograms. Medical Physics 25, 957–964 (1998)
Bovis, K.J., Singh, S.: Detection of Masses in Mammograms using Texture Measures. In: Proc.15th International Conference on Pattern Recognition, vol. 2, pp. 267–270. IEEE Press, Los Alamitos (2000)
Wiles, S., Brady, M., Highnam, R.: Comparing mammogram pairs for the detection of lesions. In: International Workshop on Digital Mammography, Kluwer, Dordrecht (1998)
Bickel, P.J., Doksum, K.A.: Mathematical statistics. Holden- Day, California (1997)
Woods, K.S.: Automated Image Analysis Techniques for Digital Mammography, Ph.D. Dissertation, University of South Florida (1994)
Kegelmeyer, W., Pruneda, J., Bourland, P., Hillis, A., Riggs, M., Nipper, M.: Computer- Aided mammographic screening for spiculated lesions. Radiology 191, 331–337 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Christoyianni, I., Constantinou, E., Dermatas, E. (2004). Automatic Detection of Abnormal Tissue in Bilateral Mammograms Using Neural Networks. In: Vouros, G.A., Panayiotopoulos, T. (eds) Methods and Applications of Artificial Intelligence. SETN 2004. Lecture Notes in Computer Science(), vol 3025. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24674-9_28
Download citation
DOI: https://doi.org/10.1007/978-3-540-24674-9_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21937-8
Online ISBN: 978-3-540-24674-9
eBook Packages: Springer Book Archive