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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/70639
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dc.contributor.authorDocusse, Tiago A.-
dc.contributor.authorFurlani, Jullyene R.-
dc.contributor.authorRomano, Rodolfo P.-
dc.contributor.authorGuido, Rodrigo C.-
dc.contributor.authorChen, Shi-Huang-
dc.contributor.authorMarranghello, Norian-
dc.contributor.authorPereira, Aledir S.-
dc.date.accessioned2014-05-27T11:23:42Z-
dc.date.accessioned2016-10-25T18:26:08Z-
dc.date.available2014-05-27T11:23:42Z-
dc.date.available2016-10-25T18:26:08Z-
dc.date.issued2008-11-24-
dc.identifierhttp://dx.doi.org/10.1109/IJCNN.2008.4634248-
dc.identifier.citationProceedings of the International Joint Conference on Neural Networks, p. 3181-3186, 2008.-
dc.identifier.urihttp://hdl.handle.net/11449/70639-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/70639-
dc.description.abstractThis paper presents a method to enhance microcalcifications and classify their borders by applying the wavelet transform. Decomposing an image and removing its low frequency sub-band the microcalcifications are enhanced. Analyzing the effects of perturbations on high frequency subband it's possible to classify its borders as smooth, rugged or undefined. Results show a false positive reduction of 69.27% using a region growing algorithm. © 2008 IEEE.en
dc.format.extent3181-3186-
dc.language.isoeng-
dc.sourceScopus-
dc.subjectWavelet transforms-
dc.subjectFalse positives-
dc.subjectHigh frequencies-
dc.subjectLow frequencies-
dc.subjectMicrocalcification-
dc.subjectMicrocalcifications-
dc.subjectRegion growing algorithms-
dc.subjectSub bands-
dc.subjectNeural networks-
dc.titleMicrocalcification enhancement and classification on mammograms using the wavelet transformen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionUniversidade de São Paulo (USP)-
dc.contributor.institutionShu-Te University-
dc.description.affiliationDepartamento de Ciências de Computação e Estatística Universidade Estadual Paulista, São José do Rio Preto, SP-
dc.description.affiliationInstituto de Física de São Carlos Universidade de São Paulo, São Carlos, SP-
dc.description.affiliationDepartment of Computer Science and Information Engineering Shu-Te University-
dc.description.affiliationUnespDepartamento de Ciências de Computação e Estatística Universidade Estadual Paulista, São José do Rio Preto, SP-
dc.identifier.doi10.1109/IJCNN.2008.4634248-
dc.identifier.wosWOS:000263827202008-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofProceedings of the International Joint Conference on Neural Networks-
dc.identifier.scopus2-s2.0-56349133254-
dc.identifier.orcid0000-0003-1086-3312pt
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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