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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/9565
Ver los metadatos del registro completo
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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 |
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/10915/9565 |
url |
http://sedici.unlp.edu.ar/handle/10915/9565 |
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-Oct07-10.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 |
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http://creativecommons.org/licenses/by-nc/3.0/ Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) |
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application/pdf 256-262 |
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