Design of segmentation algorithms to recognize interested cells in microscopy biological images

Fish fecundity is one of the most relevant parameters for estimating reproductive potential of fish stocks used for assessing stock status to guarantee a sustainable fisheries management. Fecundity is the number of matured eggs that each female fish can spawn each year. The stereological method is t...

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Detalhes bibliográficos
Autor: Mbaidin, Almoutaz Mamdooh Ahmad
Formato: tesis doctoral
Fecha de publicación:2024
País:España
Recursos:Universidad de Santiago de Compostela (USC)
Repositorio:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
Idioma:inglés
OAI Identifier:oai:minerva.usc.gal:10347/33607
Acesso em linha:http://hdl.handle.net/10347/33607
Access Level:acceso abierto
Palavra-chave:120304 Inteligencia artificial
120311 Logicales de ordenadores
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spelling Design of segmentation algorithms to recognize interested cells in microscopy biological imagesMbaidin, Almoutaz Mamdooh Ahmad120304 Inteligencia artificial120311 Logicales de ordenadoresFish fecundity is one of the most relevant parameters for estimating reproductive potential of fish stocks used for assessing stock status to guarantee a sustainable fisheries management. Fecundity is the number of matured eggs that each female fish can spawn each year. The stereological method is the most accurate technique to estimate fecundity using histological images of fish ovaries, in which matured oocytes must be measured and counted. This thesis propose a multi-scale Canny filter (MSCF) algorihm to recognize the outlines of cells. It also develop the graphical software STERapp, which includes the MSCF algorithm and other machine learning technique to help the quantitative analysis of images in the fishering labs. STERapp saves between 40% to 70% of time in the fecundity estimation.Cernadas García, EvaUniversidade de Santiago de Compostela. Escola de Doutoramento Internacional (EDIUS)20242024-01-0120242024-01-01doctoral thesishttp://purl.org/coar/resource_type/c_db06info:eu-repo/semantics/doctoralThesisapplication/pdfhttp://hdl.handle.net/10347/33607reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostelainstname:Universidad de Santiago de Compostela (USC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:minerva.usc.gal:10347/336072026-06-15T12:47:27Z
dc.title.none.fl_str_mv Design of segmentation algorithms to recognize interested cells in microscopy biological images
title Design of segmentation algorithms to recognize interested cells in microscopy biological images
spellingShingle Design of segmentation algorithms to recognize interested cells in microscopy biological images
Mbaidin, Almoutaz Mamdooh Ahmad
120304 Inteligencia artificial
120311 Logicales de ordenadores
title_short Design of segmentation algorithms to recognize interested cells in microscopy biological images
title_full Design of segmentation algorithms to recognize interested cells in microscopy biological images
title_fullStr Design of segmentation algorithms to recognize interested cells in microscopy biological images
title_full_unstemmed Design of segmentation algorithms to recognize interested cells in microscopy biological images
title_sort Design of segmentation algorithms to recognize interested cells in microscopy biological images
dc.creator.none.fl_str_mv Mbaidin, Almoutaz Mamdooh Ahmad
author Mbaidin, Almoutaz Mamdooh Ahmad
author_facet Mbaidin, Almoutaz Mamdooh Ahmad
author_role author
dc.contributor.none.fl_str_mv Cernadas García, Eva
Universidade de Santiago de Compostela. Escola de Doutoramento Internacional (EDIUS)
dc.subject.none.fl_str_mv 120304 Inteligencia artificial
120311 Logicales de ordenadores
topic 120304 Inteligencia artificial
120311 Logicales de ordenadores
description Fish fecundity is one of the most relevant parameters for estimating reproductive potential of fish stocks used for assessing stock status to guarantee a sustainable fisheries management. Fecundity is the number of matured eggs that each female fish can spawn each year. The stereological method is the most accurate technique to estimate fecundity using histological images of fish ovaries, in which matured oocytes must be measured and counted. This thesis propose a multi-scale Canny filter (MSCF) algorihm to recognize the outlines of cells. It also develop the graphical software STERapp, which includes the MSCF algorithm and other machine learning technique to help the quantitative analysis of images in the fishering labs. STERapp saves between 40% to 70% of time in the fecundity estimation.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-01-01
2024
2024-01-01
dc.type.none.fl_str_mv doctoral thesis
http://purl.org/coar/resource_type/c_db06
dc.type.openaire.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10347/33607
url http://hdl.handle.net/10347/33607
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
instname:Universidad de Santiago de Compostela (USC)
instname_str Universidad de Santiago de Compostela (USC)
reponame_str Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
collection Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
repository.name.fl_str_mv
repository.mail.fl_str_mv
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