Automated segmentation, tracking and evaluation of bacteria in microscopy images

Detalhes bibliográficos
Autor(a) principal: Queimadelas, Cátia Cristina Arranca
Data de Publicação: 2012
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/8435
Resumo: Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica
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spelling Automated segmentation, tracking and evaluation of bacteria in microscopy imagesSegmentationAlignmentGPLClassifierCARTDissertação para obtenção do Grau de Mestre em Engenharia BiomédicaMost of the investigation in microbiology relies on microscope imaging and needs to be complemented with reliable methods of computer assisted image processing, in order to avoid manual analysis. In this work, a method to assist the study of the in vivo kinetics of protein expression from Escherichia coli cells was developed. Confocal fluorescence microscopy (CFM) and Differential Interference Contrast (DIC) microscopy images were acquired and processed using the developed method. This method comprises two steps: the first one is focused on the cells detection using DIC images. The latter aligns both DIC and CFM images and computes the fluorescence level emitted by each cell. For the first step, the Gradient Path Labelling (GPL) algorithm was used which produces a moderate over-segmented DIC image. The proposed algorithm, based on decision trees generated by the Classification and Regression Trees (CART) algorithm, discards the backgrounds regions and merges the regions belonging to the same cell. To align DIC/fluorescence images an exhaustive search of the relative position and scale parameters that maximizes the fluorescence inside the cells is made. After the cells have been located on the CFM images, the fluorescence emitted by each cell is evaluated. The discard classifier performed with an error rate of 1:81% 0:98% and the merge classifier with 3:25% 1:37%. The segmentation algorithm detected 93:71% 2:06% of the cells in the tested images. The tracking algorithm correctly followed 64:52% 16:02% of cells and the alignment method successfully aligned all the tested images.Faculdade de Ciências e TecnologiaFonseca, JoséMora, AndréRibeiro, AndréRUNQueimadelas, Cátia Cristina Arranca2013-01-04T15:55:11Z20122012-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/8435enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T03:41:03Zoai:run.unl.pt:10362/8435Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:18:12.026574Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Automated segmentation, tracking and evaluation of bacteria in microscopy images
title Automated segmentation, tracking and evaluation of bacteria in microscopy images
spellingShingle Automated segmentation, tracking and evaluation of bacteria in microscopy images
Queimadelas, Cátia Cristina Arranca
Segmentation
Alignment
GPL
Classifier
CART
title_short Automated segmentation, tracking and evaluation of bacteria in microscopy images
title_full Automated segmentation, tracking and evaluation of bacteria in microscopy images
title_fullStr Automated segmentation, tracking and evaluation of bacteria in microscopy images
title_full_unstemmed Automated segmentation, tracking and evaluation of bacteria in microscopy images
title_sort Automated segmentation, tracking and evaluation of bacteria in microscopy images
author Queimadelas, Cátia Cristina Arranca
author_facet Queimadelas, Cátia Cristina Arranca
author_role author
dc.contributor.none.fl_str_mv Fonseca, José
Mora, André
Ribeiro, André
RUN
dc.contributor.author.fl_str_mv Queimadelas, Cátia Cristina Arranca
dc.subject.por.fl_str_mv Segmentation
Alignment
GPL
Classifier
CART
topic Segmentation
Alignment
GPL
Classifier
CART
description Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica
publishDate 2012
dc.date.none.fl_str_mv 2012
2012-01-01T00:00:00Z
2013-01-04T15:55:11Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/8435
url http://hdl.handle.net/10362/8435
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Faculdade de Ciências e Tecnologia
publisher.none.fl_str_mv Faculdade de Ciências e Tecnologia
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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