Combination of SVM knowledge for microcalcification detection in digital mammograms

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3177, Issue , 2004, Pages 359-365

  Li, Ying ^a,b   Jiang, Jianmin ^a  

^a UNIVERSITY OF BRADFORD   (United Kingdom)

^b SUN YAT SEN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 35048820122     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-28651-6_53     Document Type: Article

References (10)

1. R. M. Nishikawa: Detection of microcalcifications, in Image-Processing Techniques for Tumor Detection, R. N. Strickland, Ed. New York: Marcel Decker, 2002.
2. N. Karssemeijer: A stochastic model for automated detection calcifications in digital mammograms. in Proc. 12th Int. Conf. Information Medical Imaging, Wye, U.K.(1991) 227-238, 1991.
3. R. N. Strickland and H. L. Hahn: Wavelet transforms for detecting microcalcifications in mammograms. IEEE Trans. Med. Imag., 15, 218-229, 1996.
4. H. Cheng, Y. M. Liu, and R. I. Freimanis: A novel approach to microcalcifications detection using fuzzy logic techniques. IEEE Trans. Med. Imag., 17, 442-450, 1998.
5. S. Yu and L. Guan: A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films. IEEE Trans. Med. Imag., 19, 115-126, 2000.
6. V. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
7. A. Bazzani, A. Bevilacqua, D. Bollini, R. Brancaccio, R. Campanini, N.Lanconelli, A. iccardi, and D. Romani: An SVM classifier to separate false signals from microcalcifications in digital mammograms. Phys.Med. Biol., 46, 1651-1663, 2001.
8. El-Naqa; I., Yongyi Yang, M.N. Wernick, N.P. Galatsanos, and R.M. Nishikawa: A support vector machine approach for detection of microcalcifications. IEEE Transactions on Medical Imaging, 21, 1552-1563, 2002.
9. M. Heath, KW Bowyer and D. Kopans et al: Current status of the Digital Database for Screening Mammography. Digital Mammography, Kluwer Academic Publishers, 457-460, 1998.
10. Y. Li, J. Jiang and R. Qahwaji empirical study of SVM classification of pixels into ROIs in digital mammograms, To be presented at IWDM04: International Workshop on Digital Mammography, North Carolina, USA, June 18-21, 2004.