MC-Kmeans: an approach to cell image segmentation using clustering algorithms

Digital image processing has been a fundamental tool for the diagnostic and treatment of diseases. Several techniques have been used to analyze microscopic images in cell-level processes. Different methods for the segmentation task are recognized for its contribution in the image processing. Neverth...

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Autores:
Gamarra, Margarita
Manjarres, Yesit
Torres Torres, Melitsa
Escorcia-Gutierrez, Jose
Zurek, Eduardo
Tipo de recurso:
http://purl.org/coar/resource_type/c_816b
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7908
Acceso en línea:
https://hdl.handle.net/11323/7908
https://repositorio.cuc.edu.co/
Palabra clave:
Marker-controlled watershed
K-means
Cell segmentation
Digital image processing
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_e09c20e7e4af480c3afb2abbc4a32bfa
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7908
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv MC-Kmeans: an approach to cell image segmentation using clustering algorithms
title MC-Kmeans: an approach to cell image segmentation using clustering algorithms
spellingShingle MC-Kmeans: an approach to cell image segmentation using clustering algorithms
Marker-controlled watershed
K-means
Cell segmentation
Digital image processing
title_short MC-Kmeans: an approach to cell image segmentation using clustering algorithms
title_full MC-Kmeans: an approach to cell image segmentation using clustering algorithms
title_fullStr MC-Kmeans: an approach to cell image segmentation using clustering algorithms
title_full_unstemmed MC-Kmeans: an approach to cell image segmentation using clustering algorithms
title_sort MC-Kmeans: an approach to cell image segmentation using clustering algorithms
dc.creator.fl_str_mv Gamarra, Margarita
Manjarres, Yesit
Torres Torres, Melitsa
Escorcia-Gutierrez, Jose
Zurek, Eduardo
dc.contributor.author.spa.fl_str_mv Gamarra, Margarita
Manjarres, Yesit
Torres Torres, Melitsa
Escorcia-Gutierrez, Jose
Zurek, Eduardo
dc.subject.spa.fl_str_mv Marker-controlled watershed
K-means
Cell segmentation
Digital image processing
topic Marker-controlled watershed
K-means
Cell segmentation
Digital image processing
description Digital image processing has been a fundamental tool for the diagnostic and treatment of diseases. Several techniques have been used to analyze microscopic images in cell-level processes. Different methods for the segmentation task are recognized for its contribution in the image processing. Nevertheless, not all are useful in the studies at a microscopic level. In most of the biomedical images, cells are visually clustered and this makes that, simple and fast algorithms which are used in the other cases, may fail. This research proposes the development of a segmentation algorithm in HEp-2 cells type, using the marker-controlled watershed and k-means methods. This approach achieves an improvement in the cell segmentation, which allows obtaining effective information in the posterior analysis. We obtained a precision of 82.3% in the performance and in the qualitative analysis the method reached an outstanding performance in comparison with the other segmentation techniques used in the experiments. Finally, we concluded that the algorithm proposed, is suitable for the segmentation of the studied cells.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-02-23T18:38:59Z
dc.date.available.none.fl_str_mv 2021-02-23T18:38:59Z
dc.date.issued.none.fl_str_mv 2021
dc.type.spa.fl_str_mv Pre-Publicación
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_816b
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/preprint
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 0974-0635
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7908
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 0974-0635
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/7908
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.spa.fl_str_mv CC0 1.0 Universal
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/publicdomain/zero/1.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
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eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Corporación Universidad de la Costa
dc.source.spa.fl_str_mv International Journal of Artificial Intelligence
institution Corporación Universidad de la Costa
dc.source.url.spa.fl_str_mv http://www.ceser.in/ceserp/index.php/ijai/article/view/6646
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spelling Gamarra, MargaritaManjarres, YesitTorres Torres, MelitsaEscorcia-Gutierrez, JoseZurek, Eduardo2021-02-23T18:38:59Z2021-02-23T18:38:59Z20210974-0635https://hdl.handle.net/11323/7908Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Digital image processing has been a fundamental tool for the diagnostic and treatment of diseases. Several techniques have been used to analyze microscopic images in cell-level processes. Different methods for the segmentation task are recognized for its contribution in the image processing. Nevertheless, not all are useful in the studies at a microscopic level. In most of the biomedical images, cells are visually clustered and this makes that, simple and fast algorithms which are used in the other cases, may fail. This research proposes the development of a segmentation algorithm in HEp-2 cells type, using the marker-controlled watershed and k-means methods. This approach achieves an improvement in the cell segmentation, which allows obtaining effective information in the posterior analysis. We obtained a precision of 82.3% in the performance and in the qualitative analysis the method reached an outstanding performance in comparison with the other segmentation techniques used in the experiments. Finally, we concluded that the algorithm proposed, is suitable for the segmentation of the studied cells.Gamarra, Margarita-will be generated-orcid-0000-0003-1834-2984-600Manjarres, YesitTorres Torres, Melitsa-will be generated-orcid-0000-0002-5246-8073-600Escorcia-Gutierrez, Jose-will be generated-orcid-0000-0003-0518-3187-600Zurek, Eduardo-will be generated-orcid-0000-0002-9816-6863-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2International Journal of Artificial Intelligencehttp://www.ceser.in/ceserp/index.php/ijai/article/view/6646Marker-controlled watershedK-meansCell segmentationDigital image processingMC-Kmeans: an approach to cell image segmentation using clustering 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