Comparing marker definition algorithms for watershed segmentation in microscopy images

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 extrem...

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Detalles Bibliográficos
Autores principales: González, Mariela A., Cuadrado, Teresita R., Ballarín, Virginia Laura
Formato: Articulo
Lenguaje:Inglés
Publicado: 2008
Materias:
Ciencias Informáticas
Clustering
Segmentation
Image processing software
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/9639
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct08-4.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-9639
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Clustering
Segmentation
Image processing software
spellingShingle Ciencias Informáticas
Clustering
Segmentation
Image processing software
González, Mariela A.
Cuadrado, Teresita R.
Ballarín, Virginia Laura
Comparing marker definition algorithms for watershed segmentation in microscopy images
topic_facet Ciencias Informáticas
Clustering
Segmentation
Image processing software
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.
format Articulo
Articulo
author González, Mariela A.
Cuadrado, Teresita R.
Ballarín, Virginia Laura
author_facet González, Mariela A.
Cuadrado, Teresita R.
Ballarín, Virginia Laura
author_sort González, Mariela A.
title Comparing marker definition algorithms for watershed segmentation in microscopy images
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
publishDate 2008
url http://sedici.unlp.edu.ar/handle/10915/9639
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct08-4.pdf
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AT cuadradoteresitar comparingmarkerdefinitionalgorithmsforwatershedsegmentationinmicroscopyimages
AT ballarinvirginialaura comparingmarkerdefinitionalgorithmsforwatershedsegmentationinmicroscopyimages
bdutipo_str Repositorios
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