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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/9502
Ver los metadatos del registro completo
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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 http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/9502 |
url |
http://sedici.unlp.edu.ar/handle/10915/9502 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr05-2.pdf 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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reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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