Barrett’s esophagus analysis using surf features

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorOstbayerische Technische Hochschule Regensburg (OTH Regensburg)-
Autor(es): dc.contributorOTH Regensburg and Regensburg University-
Autor(es): dc.creatorSouza, Luis-
Autor(es): dc.creatorHook, Christian-
Autor(es): dc.creatorPapa, João P.-
Autor(es): dc.creatorPalm, Christoph-
Data de aceite: dc.date.accessioned2025-08-21T17:15:54Z-
Data de disponibilização: dc.date.available2025-08-21T17:15:54Z-
Data de envio: dc.date.issued2022-04-30-
Data de envio: dc.date.issued2022-04-30-
Data de envio: dc.date.issued2017-01-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-662-54345-0_34-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/232609-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/232609-
Descrição: dc.descriptionThe development of adenocarcinoma in Barrett’s esophagus is difficult to detect by endoscopic surveillance of patients with signs of dysplasia. Computer assisted diagnosis of endoscopic images (CAD) could therefore be most helpful in the demarcation and classification of neoplastic lesions. In this study we tested the feasibility of a CAD method based on Speeded up Robust Feature Detection (SURF). A given database containing 100 images from 39 patients served as benchmark for feature based classification models. Half of the images had previously been diagnosed by five clinical experts as being ”cancerous”, the other half as ”non-cancerous”. Cancerous image regions had been visibly delineated (masked) by the clinicians. SURF features acquired from full images as well as from masked areas were utilized for the supervised training and testing of an SVM classifier. The predictive accuracy of the developed CAD system is illustrated by sensitivity and specificity values. The results based on full image matching where 0.78 (sensitivity) and 0.82 (specificity) were achieved, while the masked region approach generated results of 0.90 and 0.95, respectively.-
Descrição: dc.descriptionDepartment of Computing Faculty of Sciences São Paulo State University-
Descrição: dc.descriptionRegensburg Medical Image Computing (ReMIC) Ostbayerische Technische Hochschule Regensburg (OTH Regensburg)-
Descrição: dc.descriptionRegensburg Center of Biomedical Engineering (RCBE) OTH Regensburg and Regensburg University-
Descrição: dc.descriptionDepartment of Computing Faculty of Sciences São Paulo State University-
Formato: dc.format141-146-
Idioma: dc.languageen-
Relação: dc.relationInformatik aktuell-
???dc.source???: dc.sourceScopus-
Título: dc.titleBarrett’s esophagus analysis using surf features-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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