title: Classification of breast regions as mass and non-mass based on digital mammograms using taxonomic indexes and SVM.
abstract: Breast cancer is the second most common type of cancer in the world. Several computer-aided detection and diagnosis systems have been used to assist health experts identify suspicious areas that are difficult to perceive with the human eye, thus aiding in the detection and diagnosis of cancer. This work proposes a methodology for the discrimination and classification of regions extracted from mammograms as mass and non-mass. The Digital Database for Screening Mammography (DDSM) was used in this work for the acquisition of mammograms. The taxonomic diversity index (Δ) and the taxonomic distinctness (Δ(⁎)), which were originally used in ecology, were used to describe the texture of the regions of interest. These indexes were computed based on phylogenetic trees, which were applied to describe the patterns in regions of breast images. Two approaches were used for the analysis of texture: internal and external masks. A support vector machine was used to classify the regions as mass and non-mass. The proposed methodology successfully classified the masses and non-masses, with an average accuracy of 98.88%.
authors.affiliation: Federal University of Maranhão - UFMA, Applied Computing Group - NCA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580, São Luís, MA, Brazil. Electronic address: nandoservulo@gmail.com.
authors.forename: Fernando
authors.initials: F
authors.lastname: Soares Sérvulo de Oliveira
authors.affiliation: Federal University of Maranhão - UFMA, Applied Computing Group - NCA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580, São Luís, MA, Brazil. Electronic address: antoniooseas@gmail.com.
authors.forename: Antonio
authors.initials: A
authors.lastname: Oseas de Carvalho Filho
authors.affiliation: Federal University of Maranhão - UFMA, Applied Computing Group - NCA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580, São Luís, MA, Brazil. Electronic address: ari@dee.ufma.br.
authors.forename: Aristófanes
authors.initials: A
authors.lastname: Corrêa Silva
authors.affiliation: Federal University of Maranhão - UFMA, Applied Computing Group - NCA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580, São Luís, MA, Brazil. Electronic address: paiva@deinf.ufma.br.
authors.forename: Anselmo
authors.initials: A
authors.lastname: Cardoso de Paiva
authors.affiliation: Pontifical Catholic University of Rio de Janeiro - PUC-Rio, R. São Vicente, 225, Gávea, 22453-900, Rio de Janeiro, RJ, Brazil. Electronic address: mgattass@tecgraf.puc-rio.br.
authors.forename: Marcelo
authors.initials: M
authors.lastname: Gattass
mesh_terms: D001940:Breast / Q000033:anatomy & histology / Q000473:pathology*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000145:classification* / Q000379:methods*; D010363:Pattern Recognition, Automated; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D060388:Support Vector Machine; D014463:Ultrasonography
keywords: Breast cancer; Medical image; Phylogenetic trees; Taxonomic distinctness (); Taxonomic diversity index ()