A Semi-supervised statistical framework and generative snakes for IVUS analysis

One of the most important topics in computer vision is pattern recognition and classification in images. Any classification process requires from a feature extraction process and a learning technique that categorizes each data sample. However, sometimes, it is not enough to have just a classificatio...

Descripción completa

Detalles Bibliográficos
Autor: Pujol Vila, Oriol
Tipo de recurso: tesis doctoral
Fecha de publicación:2005
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:36773
Acceso en línea:https://ddd.uab.cat/record/36773
Access Level:acceso abierto
Palabra clave:Formes deformables (Informàtica)
Reconeixement de formes
id ES_bd22f5aecbfc0744ac582e71a22eacd9
oai_identifier_str oai:ddd.uab.cat:36773
network_acronym_str ES
network_name_str España
repository_id_str
spelling A Semi-supervised statistical framework and generative snakes for IVUS analysisPujol Vila, OriolFormes deformables (Informàtica)Reconeixement de formesOne of the most important topics in computer vision is pattern recognition and classification in images. Any classification process requires from a feature extraction process and a learning technique that categorizes each data sample. However, sometimes, it is not enough to have just a classification since we could need to introduce high-level knowledge constraints to obtain a meaningful classification. Deformable models are one of the possible tools to achieve this goal. This PhD thesis describes several new techniques to be used in this scenario regarding deformable models and classification theory. The definition of deformable models guided using a external potential derived from a generative model is proposed. This approach is called generative snakes. To illustrate this process parametric snakes in a texture based context are used. The extension of the former work to geodesic deformable models is done by reformulating the geometric deformation process, leading to the Stop and Go formulation. A new tool for mixing labelled and unlabelled data for semi-supervised and particularization problems is developed and validated. This new technique allows supervised and unsupervised processes to compete for each data sample, defining the supervised clustering competition scheme. These techniques are motivated by and applied to medical image analysis, in particular to Intravascular Ultrasound (IVUS) tissue segmentation and characterization. This work also studies the tissue characterization problem in IVUS images and defines a new framework for automatic plaque recognition.Universitat Autònoma de BarcelonaRadeva Ivanova, Petia 22005-01-0120052005-01-01Tesi doctoralhttp://purl.org/coar/resource_type/c_db06VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/doctoralThesisapplication/pdfhttps://ddd.uab.cat/record/36773reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest material està protegit per drets d'autor i/o drets afins. Podeu utilitzar aquest material en funció del que permet la legislació de drets d'autor i drets afins d'aplicació al vostre cas. Per a d'altres usos heu d'obtenir permís del(s) titular(s) de drets.https://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:367732026-06-06T12:50:31Z
dc.title.none.fl_str_mv A Semi-supervised statistical framework and generative snakes for IVUS analysis
title A Semi-supervised statistical framework and generative snakes for IVUS analysis
spellingShingle A Semi-supervised statistical framework and generative snakes for IVUS analysis
Pujol Vila, Oriol
Formes deformables (Informàtica)
Reconeixement de formes
title_short A Semi-supervised statistical framework and generative snakes for IVUS analysis
title_full A Semi-supervised statistical framework and generative snakes for IVUS analysis
title_fullStr A Semi-supervised statistical framework and generative snakes for IVUS analysis
title_full_unstemmed A Semi-supervised statistical framework and generative snakes for IVUS analysis
title_sort A Semi-supervised statistical framework and generative snakes for IVUS analysis
dc.creator.none.fl_str_mv Pujol Vila, Oriol
author Pujol Vila, Oriol
author_facet Pujol Vila, Oriol
author_role author
dc.contributor.none.fl_str_mv Radeva Ivanova, Petia
dc.subject.none.fl_str_mv Formes deformables (Informàtica)
Reconeixement de formes
topic Formes deformables (Informàtica)
Reconeixement de formes
description One of the most important topics in computer vision is pattern recognition and classification in images. Any classification process requires from a feature extraction process and a learning technique that categorizes each data sample. However, sometimes, it is not enough to have just a classification since we could need to introduce high-level knowledge constraints to obtain a meaningful classification. Deformable models are one of the possible tools to achieve this goal. This PhD thesis describes several new techniques to be used in this scenario regarding deformable models and classification theory. The definition of deformable models guided using a external potential derived from a generative model is proposed. This approach is called generative snakes. To illustrate this process parametric snakes in a texture based context are used. The extension of the former work to geodesic deformable models is done by reformulating the geometric deformation process, leading to the Stop and Go formulation. A new tool for mixing labelled and unlabelled data for semi-supervised and particularization problems is developed and validated. This new technique allows supervised and unsupervised processes to compete for each data sample, defining the supervised clustering competition scheme. These techniques are motivated by and applied to medical image analysis, in particular to Intravascular Ultrasound (IVUS) tissue segmentation and characterization. This work also studies the tissue characterization problem in IVUS images and defines a new framework for automatic plaque recognition.
publishDate 2005
dc.date.none.fl_str_mv 2
2005-01-01
2005
2005-01-01
dc.type.none.fl_str_mv Tesi doctoral
http://purl.org/coar/resource_type/c_db06
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/36773
url https://ddd.uab.cat/record/36773
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
https://rightsstatements.org/vocab/InC/1.0/
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
https://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Autònoma de Barcelona
publisher.none.fl_str_mv Universitat Autònoma de Barcelona
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869418175382159360
score 15,300719

Similares en LA Referencia