Vessel Optimal Transport for Automated Alignment of Retinal Fundus Images

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorMotta, Danilo-
Autor(es): dc.creatorCasaca, Wallace [UNESP]-
Autor(es): dc.creatorPaiva, Afonso-
Data de aceite: dc.date.accessioned2022-02-22T00:56:47Z-
Data de disponibilização: dc.date.available2022-02-22T00:56:47Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2019-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/TIP.2019.2925287-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/209506-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/209506-
Descrição: dc.descriptionOptimal transport has emerged as a promising and useful tool for supporting modern image processing applications such as medical imaging and scientific visualization. Indeed, the optimal transport theory enables great flexibility in modeling problems related to image registration, as different optimization resources can be successfully used as well as the choice of suitable matching models to align the images. In this paper, we introduce an automated framework for fundus image registration which unifies optimal transport theory, image processing tools, and graph matching schemes into a functional and concise methodology. Given two ocular fundus images, we construct representative graphs which embed in their structures spatial and topological information from the eye's blood vessels. The graphs produced are then used as input by our optimal transport model in order to establish a correspondence between their sets of nodes. Finally, geometric transformations are performed between the images so as to accomplish the registration task properly. Our formulation relies on the solid mathematical foundation of optimal transport as a constrained optimization problem, being also robust when dealing with outliers created during the matching stage. We demonstrate the accuracy and effectiveness of the present framework throughout a comprehensive set of qualitative and quantitative comparisons against several influential state-of-the-art methods on various fundus image databases.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionUniv Sao Paulo, ICMC, BR-13566590 Sao Carlos, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, CCEE, BR-16750000 Rosana, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, CCEE, BR-16750000 Rosana, Brazil-
Descrição: dc.descriptionCNPq: 301642/2017-6-
Descrição: dc.descriptionFAPESP: 2019/13165-4-
Descrição: dc.descriptionFAPESP: 2014/09546-9-
Descrição: dc.descriptionFAPESP: 2013/07375-0-
Formato: dc.format6154-6168-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-inst Electrical Electronics Engineers Inc-
Relação: dc.relationIeee Transactions On Image Processing-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectRetinal image registration-
Palavras-chave: dc.subjectimage alignment-
Palavras-chave: dc.subjectblood vessel detection-
Palavras-chave: dc.subjectoptimal transport-
Título: dc.titleVessel Optimal Transport for Automated Alignment of Retinal Fundus Images-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.