A Support Vector Method for Estimating Joint Density of Medical Images

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.