Automatic segmentation and classification of human intestinal parasites from microscopy images
dc.contributor.author | Suzuki, Celso T. N. | |
dc.contributor.author | Gomes, Jancarlo F. | |
dc.contributor.author | Falcão, Alexandre X. | |
dc.contributor.author | Papa, João Paulo [UNESP] | |
dc.contributor.author | Hoshino-Shimizu, Sumie | |
dc.contributor.institution | Universidade Estadual de Campinas (UNICAMP) | |
dc.contributor.institution | Universidade Estadual Paulista (Unesp) | |
dc.contributor.institution | Universidade de São Paulo (USP) | |
dc.date.accessioned | 2014-05-27T11:30:49Z | |
dc.date.available | 2014-05-27T11:30:49Z | |
dc.date.issued | 2013-10-01 | |
dc.description.abstract | Human 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.affiliation | Institute of Computing University of Campinas, São Paulo 13084-971 | |
dc.description.affiliation | Department of Computer Science Universidade Estadual Paulista, Bauru, São Paulo 05508-900 | |
dc.description.affiliation | Faculty of Pharmaceutical Science University of São Paulo, São Paulo 66318 | |
dc.description.affiliationUnesp | Department of Computer Science Universidade Estadual Paulista, Bauru, São Paulo 05508-900 | |
dc.format.extent | 803-812 | |
dc.identifier | http://dx.doi.org/10.1109/TBME.2012.2187204 | |
dc.identifier.citation | IEEE Transactions on Biomedical Engineering, v. 60, n. 3, p. 803-812, 2013. | |
dc.identifier.doi | 10.1109/TBME.2012.2187204 | |
dc.identifier.issn | 0018-9294 | |
dc.identifier.issn | 1558-2531 | |
dc.identifier.lattes | 9039182932747194 | |
dc.identifier.scopus | 2-s2.0-84884553022 | |
dc.identifier.uri | http://hdl.handle.net/11449/76747 | |
dc.identifier.wos | WOS:000316810900026 | |
dc.language.iso | eng | |
dc.relation.ispartof | IEEE Transactions on Biomedical Engineering | |
dc.relation.ispartofjcr | 4.288 | |
dc.relation.ispartofsjr | 1,267 | |
dc.rights.accessRights | Acesso restrito | |
dc.source | Scopus | |
dc.subject | Image foresting transform (IFT) | |
dc.subject | Image segmentation | |
dc.subject | Intestinal parasitosis | |
dc.subject | Microscopy image analysis | |
dc.subject | Optimumpath forest (OPF) classifier | |
dc.subject | Pattern recognition | |
dc.title | Automatic segmentation and classification of human intestinal parasites from microscopy images | en |
dc.type | Artigo | |
dcterms.license | http://www.ieee.org/publications_standards/publications/rights/rights_policies.html | |
unesp.author.lattes | 9039182932747194 | |
unesp.author.orcid | 0000-0002-6494-7514[4] | |
unesp.campus | Universidade Estadual Paulista (Unesp), Faculdade de Ciências, Bauru | pt |
unesp.department | Computação - FC | pt |