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

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...

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Detalles Bibliográficos
Autores principales: Salas-Gonzalez, D., Górriz, J. M., Ramírez, J., Padilla, P., Illán, I. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3665226/
https://www.ncbi.nlm.nih.gov/pubmed/23762198
http://dx.doi.org/10.1155/2013/760903
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author Salas-Gonzalez, D.
Górriz, J. M.
Ramírez, J.
Padilla, P.
Illán, I. A.
author_facet Salas-Gonzalez, D.
Górriz, J. M.
Ramírez, J.
Padilla, P.
Illán, I. A.
author_sort Salas-Gonzalez, D.
collection PubMed
description 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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spelling pubmed-36652262013-06-12 Improving the Convergence Rate in Affine Registration of PET and SPECT Brain Images Using Histogram Equalization Salas-Gonzalez, D. Górriz, J. M. Ramírez, J. Padilla, P. Illán, I. A. Comput Math Methods Med Research Article 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. Hindawi Publishing Corporation 2013 2013-05-12 /pmc/articles/PMC3665226/ /pubmed/23762198 http://dx.doi.org/10.1155/2013/760903 Text en Copyright © 2013 D. Salas-Gonzalez et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Salas-Gonzalez, D.
Górriz, J. M.
Ramírez, J.
Padilla, P.
Illán, I. A.
Improving the Convergence Rate in Affine Registration of PET and SPECT Brain Images Using Histogram Equalization
title Improving the Convergence Rate in Affine Registration of PET and SPECT Brain Images Using Histogram Equalization
title_full Improving the Convergence Rate in Affine Registration of PET and SPECT Brain Images Using Histogram Equalization
title_fullStr Improving the Convergence Rate in Affine Registration of PET and SPECT Brain Images Using Histogram Equalization
title_full_unstemmed Improving the Convergence Rate in Affine Registration of PET and SPECT Brain Images Using Histogram Equalization
title_short Improving the Convergence Rate in Affine Registration of PET and SPECT Brain Images Using Histogram Equalization
title_sort improving the convergence rate in affine registration of pet and spect brain images using histogram equalization
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3665226/
https://www.ncbi.nlm.nih.gov/pubmed/23762198
http://dx.doi.org/10.1155/2013/760903
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