Abstract
Classifier plays an important role in a system detecting abnormal shadows from mammograms. In this paper, we propose the novel classification system that cascades four weak classifiers and a classifier ensemble to improve both computational cost and classification accuracy. The first several weak classifiers eliminate a large number of false positives in a short time which are easy to distinguish from abnormal regions, and the final classifier ensemble focuses on the remaining candidate regions difficult to classify, which results in high accuracy. We also show the experimental results using 2,564 mammograms.
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
Kobatake, H., Murakami, M., Takeo, H., Nawano, S.: Computerized detection of malignant tumor on mammograms. IEEE Trans. Med. Imag. 18(5), 369–378 (1999)
Furuya, S., Wei, J., Hagihara, Y., Shimizu, A., Kobatake, H., Nawano, S.: Improvement of performance to discriminate malignant tumors from normal tissue on mammograms by feature selection and evaluation of feature selection criteria. Syst. and Comp. in Japan 35(5), 72–84 (2004)
Nemoto, M., Shimizu, A., Kobatake, H., Takeo, H., Nawano, S.: Classifier ensemble for mammography CAD system combining feature selection with ensemble learning. In: Proc. 19th CARS, pp. 1047–1051 (2005)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of a simple feature. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 511–518 (2001)
Wei, J., Hagihara, Y., Kobatake, H.: Detection of cancerous tumors on chest X-ray images – Candidate detection filter and its application. In: Proc. ICIP, no.27AP4.2 (1999)
Kass, M., Witkin, A., Terzopoulos, D.: SNAKES: Active contour models. In: Proc. 1st ICNN 1987, pp. 259–268 (1987)
Pudli, P., Ferri, F.J., Novovicova, J., Kittler, J.: Floating search methods for feature selection with nonmonotomic criterion functions. In: Proc. IEEE Int. Conf. on Pattern Recognition, pp. 279–283 (1994)
Nemoto, M., Shimizu, A., Hagihara, Y., Kobatake, H., Takeo, H., Nawano, S.: Improvement of tumor detection performance in mammograms by feature selection from a large number of features and proposal of fast feature selection method. Syst. and Comp. in Japan (to appear)
Doppman, J.L., Girton, M., Vermess, M.: The risk of hepatic artery embolization in the presence of obstructive jaundice. Radiology 143, 37–43 (1982)
Hanley, J.A., McNeil, B.J.: A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148, 839–843 (1983)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nemoto, M., Shimizu, A., Kobatake, H., Takeo, H., Nawano, S. (2006). Study on Cascade Classification in Abnormal Shadow Detection for Mammograms. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, vol 4046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11783237_44
Download citation
DOI: https://doi.org/10.1007/11783237_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35625-7
Online ISBN: 978-3-540-35627-1
eBook Packages: Computer ScienceComputer Science (R0)