Abstract
A computer-aided diagnosis (CAD) system has been examined to reduce the effort of radiologist. In the mammogram, it is helpful to improve the diagnostic accuracy of malignancy microcalcifications in early stage of detecting breast cancer. In this paper, we propose a microcalcification detection method using multi-layer support vector machine (SVM) classifiers to determine whether microcalcifications are malignant or benign tumors. The proposed microcalcification detection is divided into two steps, each of which uses a SVM classifier. First, potential ROIs (Region of interest) those are suspicious as malignant tumors are detected as a coarse detection level. And then, each ROI is classified whether it is malignant or not. The proposed algorithm is applied to the Korean digital mammogram. Experimental result showed that the proposed method would outperform conventional method using ANN (artificial neural networks).
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
Yunhee Choi, Yeon Ju Kim, Hai-Rim Shin, Keun-Young Yoo. (2005) Long-term prediction of female breast cancer mortality in Korea, Asian Pacific J Cancer Prev., 2005,vol.6, pp 16–21
P. C. Johns and M. J. Yaffe (1987) X-ray characterization of normal and neoplastic breast tissues, Physics in Medicine and Biology, vol.32, No.6, 1987, pp 675
Johns, P. C., Yaffe, M. J. (1987) X-ray characterization of normal and neoplastic breast tissues, Physics in Medicine and Biology.1987 vol.32, No.6 pp.675–695
William E. Polakowski, Donald A. Cournoyer, Steven K. Rogers, Martin P. DeSimio, Dennis W. Ruck, Jeffrey W. Hoffmeister, Richard A. Raines (1997) Computer-Aided Breast Cancer Detection and Diagnosis of Masses Using Difference of Gaussians and Derivative-Based Feature Saliency, IEEE Trans. Medical Imaging vol.16, No.6
ACR National Mammography Database(NMD) at http://www.acr.org/
Hokyung Kang, Yong Man Ro, Sung Min Kim (2005) A Microcalcification Detection Using Adaptive Contrast Enhancement on Wavelet Transform and Neural Network, IEICE Trans. on Information & Systems 2005 vol.E89-D, No.3, pp.1280–1287
V. N. Vapnik (1995) The Nature of Statistical Learning Theory, Springer
El-Naqa. I., Yongyi Yang, Wernick. M.N., Galatsanos. N.P., Nishikawa. R.M. (2002) A support vector machine approach for detection of microcalcification, IEEE Trans. Medical Imaging, vol. 21, pp.1552–1563
Armando Bazzani, Alessandro Bevilacqua, Dante Bollini, Rosa Brancaccio, Renato Campanini, Nico Lanconelli, Alessandro Riccardi, Davide Romani (2001) An SVM classifier to separate false signals from microcalcifications in digital mammogram, Phys.Med. Biol.,2001, 46:1651–1663
Author information
Authors and Affiliations
Corresponding author
Editor information
Rights and permissions
Copyright information
© 2007 International Federation for Medical and Biological Engineering
About this paper
Cite this paper
Kwon, J.W., Kang, H., Ro, Y.M., Kim, S.M. (2007). A Microcalcification Detection Using Multi-Layer Support Vector Machine in Korean Digital Mammogram. In: Magjarevic, R., Nagel, J.H. (eds) World Congress on Medical Physics and Biomedical Engineering 2006. IFMBE Proceedings, vol 14. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36841-0_586
Download citation
DOI: https://doi.org/10.1007/978-3-540-36841-0_586
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36839-7
Online ISBN: 978-3-540-36841-0
eBook Packages: EngineeringEngineering (R0)