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

D. SalasGonzalez | Zendy; J. M. Górriz | Zendy; Javier Ramı́rez | Zendy; Pablo Padilla | Zendy; I. Álvarez | Zendy

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

Author(s) -

D. SalasGonzalez,

J. M. Górriz,

Javier Ramı́rez,

Pablo Padilla,

I. Álvarez

Publication year - 2013

Publication title -

computational and mathematical methods in medicine

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.462

H-Index - 48

eISSN - 1748-6718

pISSN - 1748-670X

DOI - 10.1155/2013/760903

Subject(s) - affine transformation , histogram equalization , artificial intelligence , image registration , histogram , computer science , adaptive histogram equalization , rate of convergence , preprocessor , computer vision , pattern recognition (psychology) , algorithm , mathematics , image (mathematics) , computer network , channel (broadcasting) , pure mathematics

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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