Automatic Spot Adressing in cDNA Microarray Images

Autores
Larese, Mónica G.; Gómez, Juan Carlos
Año de publicación
2008
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Complementary DNA (cDNA) microarrays are a powerful high throughput technology developed in the last decade allowing researchers to analyze the behaviour and interaction of thousands of genes simultaneously. The large amount of information provided by microarray images requires automatic techniques to develop accurate and efficient processing. Each spot in the microarray contains the hybridization level of a single gene. One of the most important features of these images are the regularity and pseudo-periodicity implicit in the spot arrangement. In this paper, an automatic approach based on texture analysis characterization techniques is proposed to localize spots in microarray images. The method estimates the displacement vectors which characterize the texture (i.e. the spot arrangement). This is achieved by means of applying the generalized Hough transform on the 2D autocorrelation function previously segmented via morphological operations. The obtained displacement vectors are used to generate a grid template which overlaps the original image. The Root-Mean-Square-Error (RMSE) between the estimated locations and the ones computed via a semiautomatic tool is calculated to evaluate the accuracy of the process. The method yields promising results.
Facultad de Informática
Materia
Ciencias Informáticas
bioinformatics
cDNA microarrays
image analysis
automatic addressing
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:10915/9625

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network_name_str SEDICI (UNLP)
spelling Automatic Spot Adressing in cDNA Microarray ImagesLarese, Mónica G.Gómez, Juan CarlosCiencias InformáticasbioinformaticscDNA microarraysimage analysisautomatic addressingComplementary DNA (cDNA) microarrays are a powerful high throughput technology developed in the last decade allowing researchers to analyze the behaviour and interaction of thousands of genes simultaneously. The large amount of information provided by microarray images requires automatic techniques to develop accurate and efficient processing. Each spot in the microarray contains the hybridization level of a single gene. One of the most important features of these images are the regularity and pseudo-periodicity implicit in the spot arrangement. In this paper, an automatic approach based on texture analysis characterization techniques is proposed to localize spots in microarray images. The method estimates the displacement vectors which characterize the texture (i.e. the spot arrangement). This is achieved by means of applying the generalized Hough transform on the 2D autocorrelation function previously segmented via morphological operations. The obtained displacement vectors are used to generate a grid template which overlaps the original image. The Root-Mean-Square-Error (RMSE) between the estimated locations and the ones computed via a semiautomatic tool is calculated to evaluate the accuracy of the process. The method yields promising results.Facultad de Informática2008-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf64-70http://sedici.unlp.edu.ar/handle/10915/9625enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Jul08-2.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:UNLP2025-10-22T16:32:21Zoai:sedici.unlp.edu.ar:10915/9625Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:32:21.7SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Automatic Spot Adressing in cDNA Microarray Images
title Automatic Spot Adressing in cDNA Microarray Images
spellingShingle Automatic Spot Adressing in cDNA Microarray Images
Larese, Mónica G.
Ciencias Informáticas
bioinformatics
cDNA microarrays
image analysis
automatic addressing
title_short Automatic Spot Adressing in cDNA Microarray Images
title_full Automatic Spot Adressing in cDNA Microarray Images
title_fullStr Automatic Spot Adressing in cDNA Microarray Images
title_full_unstemmed Automatic Spot Adressing in cDNA Microarray Images
title_sort Automatic Spot Adressing in cDNA Microarray Images
dc.creator.none.fl_str_mv Larese, Mónica G.
Gómez, Juan Carlos
author Larese, Mónica G.
author_facet Larese, Mónica G.
Gómez, Juan Carlos
author_role author
author2 Gómez, Juan Carlos
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
bioinformatics
cDNA microarrays
image analysis
automatic addressing
topic Ciencias Informáticas
bioinformatics
cDNA microarrays
image analysis
automatic addressing
dc.description.none.fl_txt_mv Complementary DNA (cDNA) microarrays are a powerful high throughput technology developed in the last decade allowing researchers to analyze the behaviour and interaction of thousands of genes simultaneously. The large amount of information provided by microarray images requires automatic techniques to develop accurate and efficient processing. Each spot in the microarray contains the hybridization level of a single gene. One of the most important features of these images are the regularity and pseudo-periodicity implicit in the spot arrangement. In this paper, an automatic approach based on texture analysis characterization techniques is proposed to localize spots in microarray images. The method estimates the displacement vectors which characterize the texture (i.e. the spot arrangement). This is achieved by means of applying the generalized Hough transform on the 2D autocorrelation function previously segmented via morphological operations. The obtained displacement vectors are used to generate a grid template which overlaps the original image. The Root-Mean-Square-Error (RMSE) between the estimated locations and the ones computed via a semiautomatic tool is calculated to evaluate the accuracy of the process. The method yields promising results.
Facultad de Informática
description Complementary DNA (cDNA) microarrays are a powerful high throughput technology developed in the last decade allowing researchers to analyze the behaviour and interaction of thousands of genes simultaneously. The large amount of information provided by microarray images requires automatic techniques to develop accurate and efficient processing. Each spot in the microarray contains the hybridization level of a single gene. One of the most important features of these images are the regularity and pseudo-periodicity implicit in the spot arrangement. In this paper, an automatic approach based on texture analysis characterization techniques is proposed to localize spots in microarray images. The method estimates the displacement vectors which characterize the texture (i.e. the spot arrangement). This is achieved by means of applying the generalized Hough transform on the 2D autocorrelation function previously segmented via morphological operations. The obtained displacement vectors are used to generate a grid template which overlaps the original image. The Root-Mean-Square-Error (RMSE) between the estimated locations and the ones computed via a semiautomatic tool is calculated to evaluate the accuracy of the process. The method yields promising results.
publishDate 2008
dc.date.none.fl_str_mv 2008-07
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info:eu-repo/semantics/publishedVersion
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format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/9625
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dc.language.none.fl_str_mv eng
language 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
64-70
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