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

الملخص EN

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.