Tomography reconstruction by entropy maximization with smoothing filtering

Autores
Barbuzza, Rosana Graciela; Lotito, Pablo Andres; Clausse, Alejandro
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The maximum entropy method (MEM) is a consistent way to treat the problem of tomography reconstruction where an image should be selected from a set of images that fit the measurement data. In this article, MEM is applied to image reconstruction from projections using an entropy formula modified by adding filter terms in order to eliminate the local noise. Numerical experiments were performed showing good results with local mean-square filter terms. The projection error can be used to estimate the weight of the filter term, providing a practical procedure to get improved solutions with limited sets of projections.
Fil: Barbuzza, Rosana Graciela. Universidad Nacional del Centro de la Pcia.de Bs.as.. Facultad de Cs.exactas. Departamento de Sistemas; Argentina
Fil: Lotito, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Departamento de Matemática; Argentina
Fil: Clausse, Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Pcia.de Bs.as.. Facultad de Cs.exactas. Departamento de Sistemas; Argentina
Materia
MAXIMUM ENTROPY
TOMOGRAPHY
IMAGE PROCESSING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/242003
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Tomography reconstruction by entropy maximization with smoothing filteringBarbuzza, Rosana GracielaLotito, Pablo AndresClausse, AlejandroMAXIMUM ENTROPYTOMOGRAPHYIMAGE PROCESSINGhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The maximum entropy method (MEM) is a consistent way to treat the problem of tomography reconstruction where an image should be selected from a set of images that fit the measurement data. In this article, MEM is applied to image reconstruction from projections using an entropy formula modified by adding filter terms in order to eliminate the local noise. Numerical experiments were performed showing good results with local mean-square filter terms. The projection error can be used to estimate the weight of the filter term, providing a practical procedure to get improved solutions with limited sets of projections.Fil: Barbuzza, Rosana Graciela. Universidad Nacional del Centro de la Pcia.de Bs.as.. Facultad de Cs.exactas. Departamento de Sistemas; ArgentinaFil: Lotito, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Departamento de Matemática; ArgentinaFil: Clausse, Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Pcia.de Bs.as.. Facultad de Cs.exactas. Departamento de Sistemas; ArgentinaTaylor & Francis Ltd2010-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/242003Barbuzza, Rosana Graciela; Lotito, Pablo Andres; Clausse, Alejandro; Tomography reconstruction by entropy maximization with smoothing filtering; Taylor & Francis Ltd; Inverse Problems In Science And Engineering; 18; 5; 6-2010; 711-7221741-5977CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/17415977.2010.492506info:eu-repo/semantics/altIdentifier/doi/10.1080/17415977.2010.492506info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:09:26Zoai:ri.conicet.gov.ar:11336/242003instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:09:26.69CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Tomography reconstruction by entropy maximization with smoothing filtering
title Tomography reconstruction by entropy maximization with smoothing filtering
spellingShingle Tomography reconstruction by entropy maximization with smoothing filtering
Barbuzza, Rosana Graciela
MAXIMUM ENTROPY
TOMOGRAPHY
IMAGE PROCESSING
title_short Tomography reconstruction by entropy maximization with smoothing filtering
title_full Tomography reconstruction by entropy maximization with smoothing filtering
title_fullStr Tomography reconstruction by entropy maximization with smoothing filtering
title_full_unstemmed Tomography reconstruction by entropy maximization with smoothing filtering
title_sort Tomography reconstruction by entropy maximization with smoothing filtering
dc.creator.none.fl_str_mv Barbuzza, Rosana Graciela
Lotito, Pablo Andres
Clausse, Alejandro
author Barbuzza, Rosana Graciela
author_facet Barbuzza, Rosana Graciela
Lotito, Pablo Andres
Clausse, Alejandro
author_role author
author2 Lotito, Pablo Andres
Clausse, Alejandro
author2_role author
author
dc.subject.none.fl_str_mv MAXIMUM ENTROPY
TOMOGRAPHY
IMAGE PROCESSING
topic MAXIMUM ENTROPY
TOMOGRAPHY
IMAGE PROCESSING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The maximum entropy method (MEM) is a consistent way to treat the problem of tomography reconstruction where an image should be selected from a set of images that fit the measurement data. In this article, MEM is applied to image reconstruction from projections using an entropy formula modified by adding filter terms in order to eliminate the local noise. Numerical experiments were performed showing good results with local mean-square filter terms. The projection error can be used to estimate the weight of the filter term, providing a practical procedure to get improved solutions with limited sets of projections.
Fil: Barbuzza, Rosana Graciela. Universidad Nacional del Centro de la Pcia.de Bs.as.. Facultad de Cs.exactas. Departamento de Sistemas; Argentina
Fil: Lotito, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Departamento de Matemática; Argentina
Fil: Clausse, Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Pcia.de Bs.as.. Facultad de Cs.exactas. Departamento de Sistemas; Argentina
description The maximum entropy method (MEM) is a consistent way to treat the problem of tomography reconstruction where an image should be selected from a set of images that fit the measurement data. In this article, MEM is applied to image reconstruction from projections using an entropy formula modified by adding filter terms in order to eliminate the local noise. Numerical experiments were performed showing good results with local mean-square filter terms. The projection error can be used to estimate the weight of the filter term, providing a practical procedure to get improved solutions with limited sets of projections.
publishDate 2010
dc.date.none.fl_str_mv 2010-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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://hdl.handle.net/11336/242003
Barbuzza, Rosana Graciela; Lotito, Pablo Andres; Clausse, Alejandro; Tomography reconstruction by entropy maximization with smoothing filtering; Taylor & Francis Ltd; Inverse Problems In Science And Engineering; 18; 5; 6-2010; 711-722
1741-5977
CONICET Digital
CONICET
url http://hdl.handle.net/11336/242003
identifier_str_mv Barbuzza, Rosana Graciela; Lotito, Pablo Andres; Clausse, Alejandro; Tomography reconstruction by entropy maximization with smoothing filtering; Taylor & Francis Ltd; Inverse Problems In Science And Engineering; 18; 5; 6-2010; 711-722
1741-5977
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/17415977.2010.492506
info:eu-repo/semantics/altIdentifier/doi/10.1080/17415977.2010.492506
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis Ltd
publisher.none.fl_str_mv Taylor & Francis Ltd
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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