Segmentation of Medical Images using Fuzzy Mathematical Morphology

Autores
Bouchet, A.; Pastore, Juan Ignacio; Ballarín, Virginia Laura
Año de publicación
2007
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Currently, Mathematical Morphology (MM) has become a powerful tool in Digital Image Processing (DIP). It allows processing images to enhance fuzzy areas, segment objects, detect edges and analyze structures. The techniques developed for binary images are a major step forward in the application of this theory to gray level images. One of these techniques is based on fuzzy logic and on the theory of fuzzy sets. Fuzzy sets have proved to be strongly advantageous when representing inaccuracies, not only regarding the spatial localization of objects in an image but also the membership of a certain pixel to a given class. Such inaccuracies are inherent to real images either because of the presence of indefinite limits between the structures or objects to be segmented within the image due to noisy acquisitions or directly because they are inherent to the image formation methods. Our approach is to show how the fuzzy sets specifically utilized in MM have turned into a functional tool in DIP.
Facultad de Informática
Materia
Ciencias Informáticas
mathematical morphology
Fuzzy set
Segmentation
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:10915/9565

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spelling Segmentation of Medical Images using Fuzzy Mathematical MorphologyBouchet, A.Pastore, Juan IgnacioBallarín, Virginia LauraCiencias Informáticasmathematical morphologyFuzzy setSegmentationCurrently, Mathematical Morphology (MM) has become a powerful tool in Digital Image Processing (DIP). It allows processing images to enhance fuzzy areas, segment objects, detect edges and analyze structures. The techniques developed for binary images are a major step forward in the application of this theory to gray level images. One of these techniques is based on fuzzy logic and on the theory of fuzzy sets. Fuzzy sets have proved to be strongly advantageous when representing inaccuracies, not only regarding the spatial localization of objects in an image but also the membership of a certain pixel to a given class. Such inaccuracies are inherent to real images either because of the presence of indefinite limits between the structures or objects to be segmented within the image due to noisy acquisitions or directly because they are inherent to the image formation methods. Our approach is to show how the fuzzy sets specifically utilized in MM have turned into a functional tool in DIP.Facultad de Informática2007-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf256-262http://sedici.unlp.edu.ar/handle/10915/9565enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct07-10.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:10915/9565Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:50:44.4SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Segmentation of Medical Images using Fuzzy Mathematical Morphology
title Segmentation of Medical Images using Fuzzy Mathematical Morphology
spellingShingle Segmentation of Medical Images using Fuzzy Mathematical Morphology
Bouchet, A.
Ciencias Informáticas
mathematical morphology
Fuzzy set
Segmentation
title_short Segmentation of Medical Images using Fuzzy Mathematical Morphology
title_full Segmentation of Medical Images using Fuzzy Mathematical Morphology
title_fullStr Segmentation of Medical Images using Fuzzy Mathematical Morphology
title_full_unstemmed Segmentation of Medical Images using Fuzzy Mathematical Morphology
title_sort Segmentation of Medical Images using Fuzzy Mathematical Morphology
dc.creator.none.fl_str_mv Bouchet, A.
Pastore, Juan Ignacio
Ballarín, Virginia Laura
author Bouchet, A.
author_facet Bouchet, A.
Pastore, Juan Ignacio
Ballarín, Virginia Laura
author_role author
author2 Pastore, Juan Ignacio
Ballarín, Virginia Laura
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
mathematical morphology
Fuzzy set
Segmentation
topic Ciencias Informáticas
mathematical morphology
Fuzzy set
Segmentation
dc.description.none.fl_txt_mv Currently, Mathematical Morphology (MM) has become a powerful tool in Digital Image Processing (DIP). It allows processing images to enhance fuzzy areas, segment objects, detect edges and analyze structures. The techniques developed for binary images are a major step forward in the application of this theory to gray level images. One of these techniques is based on fuzzy logic and on the theory of fuzzy sets. Fuzzy sets have proved to be strongly advantageous when representing inaccuracies, not only regarding the spatial localization of objects in an image but also the membership of a certain pixel to a given class. Such inaccuracies are inherent to real images either because of the presence of indefinite limits between the structures or objects to be segmented within the image due to noisy acquisitions or directly because they are inherent to the image formation methods. Our approach is to show how the fuzzy sets specifically utilized in MM have turned into a functional tool in DIP.
Facultad de Informática
description Currently, Mathematical Morphology (MM) has become a powerful tool in Digital Image Processing (DIP). It allows processing images to enhance fuzzy areas, segment objects, detect edges and analyze structures. The techniques developed for binary images are a major step forward in the application of this theory to gray level images. One of these techniques is based on fuzzy logic and on the theory of fuzzy sets. Fuzzy sets have proved to be strongly advantageous when representing inaccuracies, not only regarding the spatial localization of objects in an image but also the membership of a certain pixel to a given class. Such inaccuracies are inherent to real images either because of the presence of indefinite limits between the structures or objects to be segmented within the image due to noisy acquisitions or directly because they are inherent to the image formation methods. Our approach is to show how the fuzzy sets specifically utilized in MM have turned into a functional tool in DIP.
publishDate 2007
dc.date.none.fl_str_mv 2007-10
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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
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Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
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Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
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256-262
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instname:Universidad Nacional de La Plata
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