Improving the Convergence Rate in Affine Registration of PET and SPECT Brain Images Using Histogram Equalization. (12th May 2013)

• Record Type: Journal Article
• Authors: Salas-Gonzalez, D.; Górriz, J. M.; Ramírez, J.; Padilla, P.; Illán, I. A.
• Other Names: Meyer-Baese Anke Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2013(2013)
• Publication Date: 2013-05-12
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2013/760903
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : 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.