A Support Vector Method for Estimating Joint Density of Medical Images

  • Jesús Serrano
  • Pedro J. García-Laencina
  • Jorge Larrey-Ruiz
  • José-Luis Sancho-Gómez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4528)


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.


Support Vector Machine Support Vector Mutual Information Image Registration Support Vector Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Jesús Serrano
    • 1
  • Pedro J. García-Laencina
    • 1
  • Jorge Larrey-Ruiz
    • 1
  • José-Luis Sancho-Gómez
    • 1
  1. 1.Dpto. Tecnologías de la Información y las Comunicaciones, Universidad Politécnica de Cartagena, Plaza del hospital 1, 30202 Cartagena (Murcia)Spain

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