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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 760903, 8 pages
Research Article

Improving the Convergence Rate in Affine Registration of PET and SPECT Brain Images Using Histogram Equalization

Department of Signal Theory Networking and Communication, University of Granada, ETSIIT, 18071 Granada, Spain

Received 15 February 2013; Accepted 12 April 2013

Academic Editor: Anke Meyer-Baese

Copyright © 2013 D. Salas-Gonzalez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A procedure to improve the convergence rate for affine registration methods of medical brain images when the images differ greatly from the template is presented. The methodology is based on a histogram matching of the source images with respect to the reference brain template before proceeding with the affine registration. The preprocessed source brain images are spatially normalized to a template using a general affine model with 12 parameters. A sum of squared differences between the source images and the template is considered as objective function, and a Gauss-Newton optimization algorithm is used to find the minimum of the cost function. Using histogram equalization as a preprocessing step improves the convergence rate in the affine registration algorithm of brain images as we show in this work using SPECT and PET brain images.