Multiscale Fractal Descriptors and Polynomial Classifier for Partial Pixels Identification in Regions of Interest of Mammographic Images

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorMartins, A. S.
dc.creatorNeves, L. A.
dc.creatorNascimento, M. Z.
dc.creatorGodoy, M. F.
dc.creatorFlores, E. L.
dc.creatorCarrijo, G. A.
dc.date2014-05-20T14:01:45Z
dc.date2016-10-25T17:08:45Z
dc.date2014-05-20T14:01:45Z
dc.date2016-10-25T17:08:45Z
dc.date2012-06-01
dc.date.accessioned2017-04-05T21:26:03Z
dc.date.available2017-04-05T21:26:03Z
dc.identifierIEEE Latin America Transactions. Piscataway: IEEE-Inst Electrical Electronics Engineers Inc, v. 10, n. 4, p. 1999-2005, 2012.
dc.identifier1548-0992
dc.identifierhttp://hdl.handle.net/11449/21795
dc.identifierhttp://acervodigital.unesp.br/handle/11449/21795
dc.identifier10.1109/TLA.2012.6272486
dc.identifierWOS:000311854600021
dc.identifierhttp://dx.doi.org/10.1109/TLA.2012.6272486
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/867307
dc.descriptionComputer systems are used to support breast cancer diagnosis, with decisions taken from measurements carried out in regions of interest (ROIs). We show that support decisions obtained from square or rectangular ROIs can to include background regions with different behavior of healthy or diseased tissues. In this study, the background regions were identified as Partial Pixels (PP), obtained with a multilevel method of segmentation based on maximum entropy. The behaviors of healthy, diseased and partial tissues were quantified by fractal dimension and multiscale lacunarity, calculated through signatures of textures. The separability of groups was achieved using a polynomial classifier. The polynomials have powerful approximation properties as classifiers to treat characteristics linearly separable or not. This proposed method allowed quantifying the ROIs investigated and demonstrated that different behaviors are obtained, with distinctions of 90% for images obtained in the Cranio-caudal (CC) and Mediolateral Oblique (MLO) views.
dc.languagepor
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relationIEEE Latin America Transactions
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMammography
dc.subjectRegions of Interest
dc.subjectPartial Pixels
dc.subjectFractal Descriptors
dc.subjectPolynomial Classifier
dc.titleMultiscale Fractal Descriptors and Polynomial Classifier for Partial Pixels Identification in Regions of Interest of Mammographic Images
dc.typeOtro


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