Full automatic framework for segmentation of MR brain image

Autores
Zheng, Chong-Xun; Lin, Pan; Yang, Yong; Gu, Jian-Wen
Año de publicación
2005
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Magnetic Resonance Imaging is one of the most important medical imaging techniques for the investigating diseases of the human brain. A novel method for automatic segmentation Magnetic resonance brain image framework is proposed in this paper. This method consists of three-step segmentation procedures step. The method first uses level set method for the non-brain structures removal. Second, the bias correction method is based on computing estimates or tissue intensity distributions variation. Finally, we consider a statistical model method based on bayesian estimation, with prior Markov random filed models, for Magnetic resonance brain image classification. The algorithm consists of an energy function, based on the Potts model, which models the segmentation of an image. The algonthm was evaluated using simulated Magnetic resonance images and real Magnetic resonance brain images.
Facultad de Informática
Materia
Ciencias Informáticas
Segmentation
level set method
Imagen por Resonancia Magnética
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/9502

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network_name_str SEDICI (UNLP)
spelling Full automatic framework for segmentation of MR brain imageZheng, Chong-XunLin, PanYang, YongGu, Jian-WenCiencias InformáticasSegmentationlevel set methodImagen por Resonancia MagnéticaMagnetic Resonance Imaging is one of the most important medical imaging techniques for the investigating diseases of the human brain. A novel method for automatic segmentation Magnetic resonance brain image framework is proposed in this paper. This method consists of three-step segmentation procedures step. The method first uses level set method for the non-brain structures removal. Second, the bias correction method is based on computing estimates or tissue intensity distributions variation. Finally, we consider a statistical model method based on bayesian estimation, with prior Markov random filed models, for Magnetic resonance brain image classification. The algorithm consists of an energy function, based on the Potts model, which models the segmentation of an image. The algonthm was evaluated using simulated Magnetic resonance images and real Magnetic resonance brain images.Facultad de Informática2005-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf6-11http://sedici.unlp.edu.ar/handle/10915/9502enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr05-2.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:50:44Zoai:sedici.unlp.edu.ar:10915/9502Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:50:44.219SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Full automatic framework for segmentation of MR brain image
title Full automatic framework for segmentation of MR brain image
spellingShingle Full automatic framework for segmentation of MR brain image
Zheng, Chong-Xun
Ciencias Informáticas
Segmentation
level set method
Imagen por Resonancia Magnética
title_short Full automatic framework for segmentation of MR brain image
title_full Full automatic framework for segmentation of MR brain image
title_fullStr Full automatic framework for segmentation of MR brain image
title_full_unstemmed Full automatic framework for segmentation of MR brain image
title_sort Full automatic framework for segmentation of MR brain image
dc.creator.none.fl_str_mv Zheng, Chong-Xun
Lin, Pan
Yang, Yong
Gu, Jian-Wen
author Zheng, Chong-Xun
author_facet Zheng, Chong-Xun
Lin, Pan
Yang, Yong
Gu, Jian-Wen
author_role author
author2 Lin, Pan
Yang, Yong
Gu, Jian-Wen
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Segmentation
level set method
Imagen por Resonancia Magnética
topic Ciencias Informáticas
Segmentation
level set method
Imagen por Resonancia Magnética
dc.description.none.fl_txt_mv Magnetic Resonance Imaging is one of the most important medical imaging techniques for the investigating diseases of the human brain. A novel method for automatic segmentation Magnetic resonance brain image framework is proposed in this paper. This method consists of three-step segmentation procedures step. The method first uses level set method for the non-brain structures removal. Second, the bias correction method is based on computing estimates or tissue intensity distributions variation. Finally, we consider a statistical model method based on bayesian estimation, with prior Markov random filed models, for Magnetic resonance brain image classification. The algorithm consists of an energy function, based on the Potts model, which models the segmentation of an image. The algonthm was evaluated using simulated Magnetic resonance images and real Magnetic resonance brain images.
Facultad de Informática
description Magnetic Resonance Imaging is one of the most important medical imaging techniques for the investigating diseases of the human brain. A novel method for automatic segmentation Magnetic resonance brain image framework is proposed in this paper. This method consists of three-step segmentation procedures step. The method first uses level set method for the non-brain structures removal. Second, the bias correction method is based on computing estimates or tissue intensity distributions variation. Finally, we consider a statistical model method based on bayesian estimation, with prior Markov random filed models, for Magnetic resonance brain image classification. The algorithm consists of an energy function, based on the Potts model, which models the segmentation of an image. The algonthm was evaluated using simulated Magnetic resonance images and real Magnetic resonance brain images.
publishDate 2005
dc.date.none.fl_str_mv 2005-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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info:ar-repo/semantics/articulo
format article
status_str publishedVersion
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dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
6-11
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instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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