Improving the convergence rate in affine registration of PET brain images using histogram matching

In this work, 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 to proceed with the affine registration. The preprocessed source brain images are spatially normalize 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 fusion PET/MRI brain images.