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.
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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
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DOI: https://doi.org/10.1007/978-3-540-73055-2_18
Publisher Name: Springer, Berlin, Heidelberg
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