Comparing marker definition algorithms for watershed segmentation in microscopy images

Autores
González, Mariela A.; Cuadrado, Teresita R.; Ballarín, Virginia Laura
Año de publicación
2008
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Segmentation is often a critical step in image analysis. Microscope image components show great variability of shapes, sizes, intensities and textures. An inaccurate segmentation conditions the ulterior quantification and parameter measurement. The Watershed Transform is able to distinguish extremely complex objects and is easily adaptable to various kinds of images. The success of the Watershed Transform depends essentially on the existence of unequivocal markers for each of the objects of interest. The standard methods of marker detection are highly specific, they have a high computational cost and they determine markers in an effective but not automatic way when processing highly textured images. This paper compares two different pattern recognition techniques proposed for the automatic detection of markers that allow the application of the Watershed Transform to biomedical images acquired via a microscope. The results allow us to conclude that the method based on clustering is an effective tool for the application of the Watershed Transform.
Facultad de Informática
Materia
Ciencias Informáticas
Clustering
Segmentation
Image processing software
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:10915/9639
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/9639
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Comparing marker definition algorithms for watershed segmentation in microscopy imagesGonzález, Mariela A.Cuadrado, Teresita R.Ballarín, Virginia LauraCiencias InformáticasClusteringSegmentationImage processing softwareSegmentation is often a critical step in image analysis. Microscope image components show great variability of shapes, sizes, intensities and textures. An inaccurate segmentation conditions the ulterior quantification and parameter measurement. The Watershed Transform is able to distinguish extremely complex objects and is easily adaptable to various kinds of images. The success of the Watershed Transform depends essentially on the existence of unequivocal markers for each of the objects of interest. The standard methods of marker detection are highly specific, they have a high computational cost and they determine markers in an effective but not automatic way when processing highly textured images. This paper compares two different pattern recognition techniques proposed for the automatic detection of markers that allow the application of the Watershed Transform to biomedical images acquired via a microscope. The results allow us to conclude that the method based on clustering is an effective tool for the application of the Watershed Transform.Facultad de Informática2008-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf151-157http://sedici.unlp.edu.ar/handle/10915/9639enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct08-4.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:10915/9639Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:50:45.18SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Comparing marker definition algorithms for watershed segmentation in microscopy images
title Comparing marker definition algorithms for watershed segmentation in microscopy images
spellingShingle Comparing marker definition algorithms for watershed segmentation in microscopy images
González, Mariela A.
Ciencias Informáticas
Clustering
Segmentation
Image processing software
title_short Comparing marker definition algorithms for watershed segmentation in microscopy images
title_full Comparing marker definition algorithms for watershed segmentation in microscopy images
title_fullStr Comparing marker definition algorithms for watershed segmentation in microscopy images
title_full_unstemmed Comparing marker definition algorithms for watershed segmentation in microscopy images
title_sort Comparing marker definition algorithms for watershed segmentation in microscopy images
dc.creator.none.fl_str_mv González, Mariela A.
Cuadrado, Teresita R.
Ballarín, Virginia Laura
author González, Mariela A.
author_facet González, Mariela A.
Cuadrado, Teresita R.
Ballarín, Virginia Laura
author_role author
author2 Cuadrado, Teresita R.
Ballarín, Virginia Laura
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Clustering
Segmentation
Image processing software
topic Ciencias Informáticas
Clustering
Segmentation
Image processing software
dc.description.none.fl_txt_mv Segmentation is often a critical step in image analysis. Microscope image components show great variability of shapes, sizes, intensities and textures. An inaccurate segmentation conditions the ulterior quantification and parameter measurement. The Watershed Transform is able to distinguish extremely complex objects and is easily adaptable to various kinds of images. The success of the Watershed Transform depends essentially on the existence of unequivocal markers for each of the objects of interest. The standard methods of marker detection are highly specific, they have a high computational cost and they determine markers in an effective but not automatic way when processing highly textured images. This paper compares two different pattern recognition techniques proposed for the automatic detection of markers that allow the application of the Watershed Transform to biomedical images acquired via a microscope. The results allow us to conclude that the method based on clustering is an effective tool for the application of the Watershed Transform.
Facultad de Informática
description Segmentation is often a critical step in image analysis. Microscope image components show great variability of shapes, sizes, intensities and textures. An inaccurate segmentation conditions the ulterior quantification and parameter measurement. The Watershed Transform is able to distinguish extremely complex objects and is easily adaptable to various kinds of images. The success of the Watershed Transform depends essentially on the existence of unequivocal markers for each of the objects of interest. The standard methods of marker detection are highly specific, they have a high computational cost and they determine markers in an effective but not automatic way when processing highly textured images. This paper compares two different pattern recognition techniques proposed for the automatic detection of markers that allow the application of the Watershed Transform to biomedical images acquired via a microscope. The results allow us to conclude that the method based on clustering is an effective tool for the application of the Watershed Transform.
publishDate 2008
dc.date.none.fl_str_mv 2008-10
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/10915/9639
url http://sedici.unlp.edu.ar/handle/10915/9639
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-Oct08-4.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
151-157
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
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
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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