Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model

In this paper, a method for medical image registration based on the bounded generalized Gaussian mixture model is proposed. The bounded generalized Gaussian mixture model is used to approach the joint intensity of source medical images. The mixture model is formulated based on a maximum likelihood f...

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Detalles Bibliográficos
Autores principales: Wang, Jingkun, Xiang, Kun, Chen, Kuo, Liu, Rui, Ni, Ruifeng, Zhu, Hao, Xiong, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9201218/
https://www.ncbi.nlm.nih.gov/pubmed/35720703
http://dx.doi.org/10.3389/fnins.2022.911957
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author Wang, Jingkun
Xiang, Kun
Chen, Kuo
Liu, Rui
Ni, Ruifeng
Zhu, Hao
Xiong, Yan
author_facet Wang, Jingkun
Xiang, Kun
Chen, Kuo
Liu, Rui
Ni, Ruifeng
Zhu, Hao
Xiong, Yan
author_sort Wang, Jingkun
collection PubMed
description In this paper, a method for medical image registration based on the bounded generalized Gaussian mixture model is proposed. The bounded generalized Gaussian mixture model is used to approach the joint intensity of source medical images. The mixture model is formulated based on a maximum likelihood framework, and is solved by an expectation-maximization algorithm. The registration performance of the proposed approach on different medical images is verified through extensive computer simulations. Empirical findings confirm that the proposed approach is significantly better than other conventional ones.
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spelling pubmed-92012182022-06-17 Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model Wang, Jingkun Xiang, Kun Chen, Kuo Liu, Rui Ni, Ruifeng Zhu, Hao Xiong, Yan Front Neurosci Neuroscience In this paper, a method for medical image registration based on the bounded generalized Gaussian mixture model is proposed. The bounded generalized Gaussian mixture model is used to approach the joint intensity of source medical images. The mixture model is formulated based on a maximum likelihood framework, and is solved by an expectation-maximization algorithm. The registration performance of the proposed approach on different medical images is verified through extensive computer simulations. Empirical findings confirm that the proposed approach is significantly better than other conventional ones. Frontiers Media S.A. 2022-06-02 /pmc/articles/PMC9201218/ /pubmed/35720703 http://dx.doi.org/10.3389/fnins.2022.911957 Text en Copyright © 2022 Wang, Xiang, Chen, Liu, Ni, Zhu and Xiong. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Wang, Jingkun
Xiang, Kun
Chen, Kuo
Liu, Rui
Ni, Ruifeng
Zhu, Hao
Xiong, Yan
Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model
title Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model
title_full Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model
title_fullStr Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model
title_full_unstemmed Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model
title_short Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model
title_sort medical image registration algorithm based on bounded generalized gaussian mixture model
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9201218/
https://www.ncbi.nlm.nih.gov/pubmed/35720703
http://dx.doi.org/10.3389/fnins.2022.911957
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