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...
- 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
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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 |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ARTOTR |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
format |
http://purl.org/coar/resource_type/c_816b |
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/ http://purl.org/coar/access_right/c_abf2 |
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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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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