Cellular outline segmentation using fractal estimators

Autores
Salvatelli, Adrián; Caropresi, José; Delrieux, Claudio; Izaguirre, María F.; Casco, Víctor
Año de publicación
2007
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Segmentation in biological images is essential for the determination of biological parameters that allow the construction of models of several biological problems. This helps to establish clear relationships between those models and the parameter estimation, and for elaboration of key experiments that give support to biological theories. Segmentation is the process of qualitative or quantitative information extraction (shape, texture, physical and geometric properties, among others). These quantities are needed to compute the biological descriptors for further classification (v.g., cell counting, development stage assessment, and many others). This process is almost always supervised (i.e., human assisted), since the quality of the images that are produced with classic microscopy technologies have defects that in general disallow the application of unsupervised segmentation techniques. In this paper we investigate the use of the a local fractal dimension estimation as an image descriptor for microscopy images. This local descriptor appears to be robust enough to perform unsupervised or semisupervised segmentations, specifically in our study. We applied this technique on microscopy images of amphibian embryos' skin in which, using immunofluorescence techniques, we have labeled the cell adhesion molecule E-Cadherin. This molecule is one of the key factors of the Ca2+- dependent cell-cell adhesion. Segmentation of the cellular outlines was performed using a processing workflow, which can be repeatedly applied to a set of similar images, from which information is extracted for characterization and eventual quantification purposes.
Facultad de Informática
Materia
Ciencias Informáticas
IMAGE PROCESSING AND COMPUTER VISION
Segmentation
Fractals
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:10915/9536

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network_name_str SEDICI (UNLP)
spelling Cellular outline segmentation using fractal estimatorsSalvatelli, AdriánCaropresi, JoséDelrieux, ClaudioIzaguirre, María F.Casco, VíctorCiencias InformáticasIMAGE PROCESSING AND COMPUTER VISIONSegmentationFractalsSegmentation in biological images is essential for the determination of biological parameters that allow the construction of models of several biological problems. This helps to establish clear relationships between those models and the parameter estimation, and for elaboration of key experiments that give support to biological theories. Segmentation is the process of qualitative or quantitative information extraction (shape, texture, physical and geometric properties, among others). These quantities are needed to compute the biological descriptors for further classification (v.g., cell counting, development stage assessment, and many others). This process is almost always supervised (i.e., human assisted), since the quality of the images that are produced with classic microscopy technologies have defects that in general disallow the application of unsupervised segmentation techniques. In this paper we investigate the use of the a local fractal dimension estimation as an image descriptor for microscopy images. This local descriptor appears to be robust enough to perform unsupervised or semisupervised segmentations, specifically in our study. We applied this technique on microscopy images of amphibian embryos' skin in which, using immunofluorescence techniques, we have labeled the cell adhesion molecule E-Cadherin. This molecule is one of the key factors of the Ca<sup>2+</sup>- dependent cell-cell adhesion. Segmentation of the cellular outlines was performed using a processing workflow, which can be repeatedly applied to a set of similar images, from which information is extracted for characterization and eventual quantification purposes.Facultad de Informática2007-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf105-111http://sedici.unlp.edu.ar/handle/10915/9536enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Mar07-17.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:UNLP2025-10-15T10:43:17Zoai:sedici.unlp.edu.ar:10915/9536Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:43:17.822SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Cellular outline segmentation using fractal estimators
title Cellular outline segmentation using fractal estimators
spellingShingle Cellular outline segmentation using fractal estimators
Salvatelli, Adrián
Ciencias Informáticas
IMAGE PROCESSING AND COMPUTER VISION
Segmentation
Fractals
title_short Cellular outline segmentation using fractal estimators
title_full Cellular outline segmentation using fractal estimators
title_fullStr Cellular outline segmentation using fractal estimators
title_full_unstemmed Cellular outline segmentation using fractal estimators
title_sort Cellular outline segmentation using fractal estimators
dc.creator.none.fl_str_mv Salvatelli, Adrián
Caropresi, José
Delrieux, Claudio
Izaguirre, María F.
Casco, Víctor
author Salvatelli, Adrián
author_facet Salvatelli, Adrián
Caropresi, José
Delrieux, Claudio
Izaguirre, María F.
Casco, Víctor
author_role author
author2 Caropresi, José
Delrieux, Claudio
Izaguirre, María F.
Casco, Víctor
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
IMAGE PROCESSING AND COMPUTER VISION
Segmentation
Fractals
topic Ciencias Informáticas
IMAGE PROCESSING AND COMPUTER VISION
Segmentation
Fractals
dc.description.none.fl_txt_mv Segmentation in biological images is essential for the determination of biological parameters that allow the construction of models of several biological problems. This helps to establish clear relationships between those models and the parameter estimation, and for elaboration of key experiments that give support to biological theories. Segmentation is the process of qualitative or quantitative information extraction (shape, texture, physical and geometric properties, among others). These quantities are needed to compute the biological descriptors for further classification (v.g., cell counting, development stage assessment, and many others). This process is almost always supervised (i.e., human assisted), since the quality of the images that are produced with classic microscopy technologies have defects that in general disallow the application of unsupervised segmentation techniques. In this paper we investigate the use of the a local fractal dimension estimation as an image descriptor for microscopy images. This local descriptor appears to be robust enough to perform unsupervised or semisupervised segmentations, specifically in our study. We applied this technique on microscopy images of amphibian embryos' skin in which, using immunofluorescence techniques, we have labeled the cell adhesion molecule E-Cadherin. This molecule is one of the key factors of the Ca<sup>2+</sup>- dependent cell-cell adhesion. Segmentation of the cellular outlines was performed using a processing workflow, which can be repeatedly applied to a set of similar images, from which information is extracted for characterization and eventual quantification purposes.
Facultad de Informática
description Segmentation in biological images is essential for the determination of biological parameters that allow the construction of models of several biological problems. This helps to establish clear relationships between those models and the parameter estimation, and for elaboration of key experiments that give support to biological theories. Segmentation is the process of qualitative or quantitative information extraction (shape, texture, physical and geometric properties, among others). These quantities are needed to compute the biological descriptors for further classification (v.g., cell counting, development stage assessment, and many others). This process is almost always supervised (i.e., human assisted), since the quality of the images that are produced with classic microscopy technologies have defects that in general disallow the application of unsupervised segmentation techniques. In this paper we investigate the use of the a local fractal dimension estimation as an image descriptor for microscopy images. This local descriptor appears to be robust enough to perform unsupervised or semisupervised segmentations, specifically in our study. We applied this technique on microscopy images of amphibian embryos' skin in which, using immunofluorescence techniques, we have labeled the cell adhesion molecule E-Cadherin. This molecule is one of the key factors of the Ca<sup>2+</sup>- dependent cell-cell adhesion. Segmentation of the cellular outlines was performed using a processing workflow, which can be repeatedly applied to a set of similar images, from which information is extracted for characterization and eventual quantification purposes.
publishDate 2007
dc.date.none.fl_str_mv 2007-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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format article
status_str publishedVersion
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dc.language.none.fl_str_mv eng
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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
105-111
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