Descritores Fractais Multiescalas e Classificador Polinomial para Identificação de Pixels Parciais em Regiões de Interesse de Imagens Mamográficas
(Multiscale Fractal Descriptors and Polynomial Classifier for Partial Pixels Identification in Regions of Interest of Mammographic Images)
Alessandro Santana Martins (email@example.com)1, Leandro Alves Neves (firstname.lastname@example.org)2, Marcelo Zanchetta do Nascimento (email@example.com)3, Moacir Fernandes de Godoy (firstname.lastname@example.org)4, Edna Lucia Flores (email@example.com)5, Gilberto Arantes Carrijo (firstname.lastname@example.org)5
1Universidade Federal de Uberlândia (UFU) e Instituto Federal do Triangulo Mineiro (IFTM)2Universidade Estadual Paulista (UNESP)3Universidade Federal do ABC (UFABC)4Faculdade de Medicina de São José do Rio Preto (FAMERP)5Universidade Federal de Uberlândia (UFU)
This paper appears in: Revista IEEE América Latina
Publication Date: June 2012
Volume: 10, Issue: 4
Computer 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.
Mammography, Regions of Interest, Partial Pixels, Fractal Descriptors, Polynomial Classifier.
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