Cellular outline segmentation using fractal estimators

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 th...

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Autores principales: Salvatelli, Adrián, Caropresi, José, Delrieux, Claudio, Izaguirre, María F., Casco, Víctor
Formato: Articulo
Lenguaje:Inglés
Publicado: 2007
Materias:
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/9536
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Mar07-17.pdf
Aporte de:SEDICI (UNLP) de Universidad Nacional de La Plata Ver origen
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spelling I19-R120-10915-95362019-06-20T20:03:51Z http://sedici.unlp.edu.ar/handle/10915/9536 http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Mar07-17.pdf issn:1666-6038 Cellular outline segmentation using fractal estimators Salvatelli, Adrián Caropresi, José Delrieux, Claudio Izaguirre, María F. Casco, Víctor 2007-04 2008-05-22T03:00:00Z en Ciencias Informáticas IMAGE PROCESSING AND COMPUTER VISION Segmentation Fractals 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 Articulo Articulo http://creativecommons.org/licenses/by-nc/3.0/ Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) application/pdf 105-111
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
IMAGE PROCESSING AND COMPUTER VISION
Segmentation
Fractals
spellingShingle Ciencias Informáticas
IMAGE PROCESSING AND COMPUTER VISION
Segmentation
Fractals
Salvatelli, Adrián
Caropresi, José
Delrieux, Claudio
Izaguirre, María F.
Casco, Víctor
Cellular outline segmentation using fractal estimators
topic_facet Ciencias Informáticas
IMAGE PROCESSING AND COMPUTER VISION
Segmentation
Fractals
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.
format Articulo
Articulo
author Salvatelli, Adrián
Caropresi, José
Delrieux, Claudio
Izaguirre, María F.
Casco, Víctor
author_facet Salvatelli, Adrián
Caropresi, José
Delrieux, Claudio
Izaguirre, María F.
Casco, Víctor
author_sort Salvatelli, Adrián
title Cellular outline segmentation using fractal estimators
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
publishDate 2007
url http://sedici.unlp.edu.ar/handle/10915/9536
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Mar07-17.pdf
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AT delrieuxclaudio cellularoutlinesegmentationusingfractalestimators
AT izaguirremariaf cellularoutlinesegmentationusingfractalestimators
AT cascovictor cellularoutlinesegmentationusingfractalestimators
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