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