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Comput Math Methods Med. 2013;2013:760903. doi: 10.1155/2013/760903. Epub 2013 May 12.

Improving the convergence rate in affine registration of PET and SPECT brain images using histogram equalization.

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


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.

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