Abstract
Human learning inspires a large amount of algorithms and techniques to solve problems in image understanding. Supervised learning algorithms based on support vector machines are currently one of the most effective methods in machine learning. A support vector approach is used in this paper to solve a typical problem in image registration, this is, the joint probability density function estimation needed in the image registration by maximization of mutual information. Results estimating the joint probability density function for two CT and PET images demonstrate the proposed approach advantages over the classical histogram estimation.
This work is partially supported by Ministerio de Educación y Ciencia under grant TEC2006-13338/TCM, and by Consejería de Educación y Cultura de Murcia under grant 03122/PI/05.
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
Mitchel, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Vapnik, V.: The Nature of Statistical Learning Theory. Spinger, New York (1995)
Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., Marchal, G.: Automated Multi-modality Image Registration based on Information Theory. In: Bizais, Y., Barillot, C., Di Paola, R. (eds.) Proceedings XIVth International Conference on Information Processing in Medical Imaging – IPMI’95, Ile de Berder, France, June 1995. Computational Imaging and Vision, vol. 3, pp. 263–274. Kluwer Academic Publishers, Dordrecht (1995)
Viola, P., Wells, W.M.: Alignement by Maximization of Mutual Information. In: Proceedings of the 5th International Conference on Computer Vision, Cambridge, MA, pp. 16–23 (1995)
Weston, J.A.E.: Extensions to the Support Vector Method. PhD thesis, University of London (1999)
Vapnik, V., Golowich, S.E., Smola, A.: Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. In: Mozer, M.C., Jordan, M.I., Petsche, T. (eds.) Advances in Neural Information Processing Systems, vol. 9, pp. 281–287. MIT Press, Cambridge (1997)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Maintz, J.B.A., Viergever, M.A.: A survey of medical image registration. Medical Image Analisys 2(1), 1–36 (1998)
Maes, F., Vandermeulen, D., Suetens, P.: Medical Image Registration Using Mutual Information (invited paper). Proceedings of the IEEE (Special Issue on Emerging Medical Imaging Technology) 91, 1699–1722 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Serrano, J., García-Laencina, P.J., Larrey-Ruiz, J., Sancho-Gómez, JL. (2007). A Support Vector Method for Estimating Joint Density of Medical Images. In: Mira, J., Álvarez, J.R. (eds) Nature Inspired Problem-Solving Methods in Knowledge Engineering. IWINAC 2007. Lecture Notes in Computer Science, vol 4528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73055-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-73055-2_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73054-5
Online ISBN: 978-3-540-73055-2
eBook Packages: Computer ScienceComputer Science (R0)