Abstract
Fracture of the bone is a very serious medical condition. In clinical practice, a tired radiologist has been found to miss fracture cases after looking through many images containing healthy bones. Computer detection of fractures can assist the doctors by flagging suspicious cases for closer examinations and thus improve the timeliness and accuracy of their diagnosis. This paper presents a new divide-and-conquer approach for fracture detection by partitioning the problem into smaller sub-problems in SVM’s kernel space, and training an SVM to specialize in solving each sub-problem. As the sub-problems are easier to solve than the whole problem, a hierarchy of SVMs performs better than an individual SVM that solves the whole problem. Compared to existing methods, this approach enhances the accuracy and reliability of SVMs.
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
IOF: Facts and statistics about osteoporosis and its impact. International Osteoporosis Foundation (2007), www.iofbonehealth.org/facts-and-statistics.html
Tian, T.P., Chen, Y., Leow, W.K., Hsu, W., Howe, T.S., Png, M.A.: Computing neck-shaft angle of femur for x-ray fracture detection. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 82–89. Springer, Heidelberg (2003)
Lim, S.E., Xing, Y., Chen, Y., Leow, W.K., Howe, T.S., Png, M.A.: Detection of femur and radius fractures in x-ray images. In: Proc. 2nd Int. Conf. on Advances in Medical Signal and Info. Proc. (2004)
Yap, W.H., Chen, Y., Leow, W.K., Howe, T.S., Png, M.A.: Detecting femur fractures by texture analysis of trabeculae. In: Proc. ICPR (2004)
Lum, V.L.F., Leow, W.K., Chen, Y., Howe, T.S., Png, M.A.: Combining classifiers for bone fracture detection in x-ray images. In: Proc. ICIP (2005)
Kreßel, U.: Pairwise classification and support vector machines. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods Support Vector Learning, pp. 255–268. MIT Press, Cambridge (1999)
Platt, J., Cristianini, N., Shawe-Taylor, J.: Large margin DAGs for multiclass classification. In: Solla, S.A., Leen, T.K., Muller, K.R. (eds.) Advances in Neural Information Processing Systems, MIT Press, Cambridge (2000)
Hsu, C.W., Lin, C.J.: A comparison of methods for multi-class support vector machines. IEEE Trans. on Neural Networks 13, 415–425 (2002)
Li, R., Leow, W.K.: From region features to semantic labels: A probabilistic approach. In: Proc Int. Conf. on Multimedia Modeling, pp. 402–420 (2003)
Chakrabartty, S., Cauwenberghs, G.: Gini-SVM. bach.ece.jhu.edu/svm/ginisvm/
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, J.C., Leow, W.K., Howe, T.S. (2007). Hierarchical Classifiers for Detection of Fractures in X-Ray Images. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_119
Download citation
DOI: https://doi.org/10.1007/978-3-540-74272-2_119
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74271-5
Online ISBN: 978-3-540-74272-2
eBook Packages: Computer ScienceComputer Science (R0)