Automatic Segmentation and Classification of Human Intestinal Parasites From Microscopy Images

dc.creatorSuzuki, CTN
dc.creatorGomes, JF
dc.creatorFalcao, AX
dc.creatorPapa, JP
dc.creatorHoshino-Shimizu, S
dc.date2013
dc.dateMAR
dc.date2014-07-30T13:51:31Z
dc.date2015-11-26T16:39:36Z
dc.date2014-07-30T13:51:31Z
dc.date2015-11-26T16:39:36Z
dc.date.accessioned2018-03-28T23:23:14Z
dc.date.available2018-03-28T23:23:14Z
dc.identifierIeee Transactions On Biomedical Engineering. Ieee-inst Electrical Electronics Engineers Inc, v. 60, n. 3, n. 803, n. 812, 2013.
dc.identifier0018-9294
dc.identifierWOS:000316810900026
dc.identifier10.1109/TBME.2012.2187204
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/55220
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/55220
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1272526
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionHuman 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.
dc.description60
dc.description3
dc.description803
dc.description812
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFAPESP [2003/14096-8, 2008/57428-4, 2004/11218-8, 2006/61385-3, 2008/50090-8, 2009/16206-1]
dc.descriptionCNPq [481556/2009-5]
dc.languageen
dc.publisherIeee-inst Electrical Electronics Engineers Inc
dc.publisherPiscataway
dc.publisherEUA
dc.relationIeee Transactions On Biomedical Engineering
dc.relationIEEE Trans. Biomed. Eng.
dc.rightsfechado
dc.rightshttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dc.sourceWeb of Science
dc.subjectImage foresting transform (IFT)
dc.subjectimage segmentation
dc.subjectintestinal parasitosis
dc.subjectmicroscopy image analysis
dc.subjectoptimum-path forest (OPF) classifier
dc.subjectpattern recognition
dc.subjectNeural-networks
dc.subjectRecognition
dc.subjectDiagnosis
dc.subjectEggs
dc.subjectAlgorithms
dc.subjectProtozoa
dc.subjectSystem
dc.titleAutomatic Segmentation and Classification of Human Intestinal Parasites From Microscopy Images
dc.typeArtículos de revistas


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