Automatic segmentation and classification of human intestinal parasites from microscopy images

Página do item simplificado
dc.contributor.authorSuzuki, Celso T. N.
dc.contributor.authorGomes, Jancarlo F.
dc.contributor.authorFalcão, Alexandre X.
dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.authorHoshino-Shimizu, Sumie
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.date.accessioned2014-05-27T11:30:49Z
dc.date.available2014-05-27T11:30:49Z
dc.date.issued2013-10-01
dc.description.abstractHuman intestinal parasites constitute a problem in most tropical countries, causing death or physical and mental disorders. Their diagnosis usually relies on the visual analysis of microscopy images, with error rates that may range from moderate to high. The problem has been addressed via computational image analysis, but only for a few species and images free of fecal impurities. In routine, fecal impurities are a real challenge for automatic image analysis. We have circumvented this problem by a method that can segment and classify, from bright field microscopy images with fecal impurities, the 15 most common species of protozoan cysts, helminth eggs, and larvae in Brazil. Our approach exploits ellipse matching and image foresting transform for image segmentation, multiple object descriptors and their optimum combination by genetic programming for object representation, and the optimum-path forest classifier for object recognition. The results indicate that our method is a promising approach toward the fully automation of the enteroparasitosis diagnosis. © 2012 IEEE.en
dc.description.affiliationInstitute of Computing University of Campinas, São Paulo 13084-971
dc.description.affiliationDepartment of Computer Science Universidade Estadual Paulista, Bauru, São Paulo 05508-900
dc.description.affiliationFaculty of Pharmaceutical Science University of São Paulo, São Paulo 66318
dc.description.affiliationUnespDepartment of Computer Science Universidade Estadual Paulista, Bauru, São Paulo 05508-900
dc.format.extent803-812
dc.identifierhttp://dx.doi.org/10.1109/TBME.2012.2187204
dc.identifier.citationIEEE Transactions on Biomedical Engineering, v. 60, n. 3, p. 803-812, 2013.
dc.identifier.doi10.1109/TBME.2012.2187204
dc.identifier.issn0018-9294
dc.identifier.issn1558-2531
dc.identifier.lattes9039182932747194
dc.identifier.scopus2-s2.0-84884553022
dc.identifier.urihttp://hdl.handle.net/11449/76747
dc.identifier.wosWOS:000316810900026
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Biomedical Engineering
dc.relation.ispartofjcr4.288
dc.relation.ispartofsjr1,267
dc.rights.accessRightsAcesso restrito
dc.sourceScopus
dc.subjectImage foresting transform (IFT)
dc.subjectImage segmentation
dc.subjectIntestinal parasitosis
dc.subjectMicroscopy image analysis
dc.subjectOptimumpath forest (OPF) classifier
dc.subjectPattern recognition
dc.titleAutomatic segmentation and classification of human intestinal parasites from microscopy imagesen
dc.typeArtigo
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
unesp.author.lattes9039182932747194
unesp.author.orcid0000-0002-6494-7514[4]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

Arquivos

Coleções

Bauru - FC - Faculdade de Ciências