title: 3D shape analysis to reduce false positives for lung nodule detection systems.
abstract: Using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), we developed a methodology for classifying lung nodules. The proposed methodology uses image processing and pattern recognition techniques. To classify volumes of interest into nodules and non-nodules, we used shape measurements only, analyzing their shape using shape diagrams, proportion measurements, and a cylinder-based analysis. In addition, we use the support vector machine classifier. To test the proposed methodology, it was applied to 833 images from the LIDC-IDRI database, and cross-validation with k-fold, where [Formula: see text], was used to validate the results. The proposed methodology for the classification of nodules and non-nodules achieved a mean accuracy of 95.33 %. Lung cancer causes more deaths than any other cancer worldwide. Therefore, precocious detection allows for faster therapeutic intervention and a more favorable prognosis for the patient. Our proposed methodology contributes to the classification of lung nodules and should help in the diagnosis of lung cancer.
authors.affiliation: Federal University of Maranhão - UFMA, Applied Computing Group - NCA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, São Luís, MA, 65085-580, Brazil. antoniooseas@gmail.com.
authors.forename: Antonio Oseas de Carvalho
authors.initials: AOC
authors.lastname: Filho
authors.affiliation: Federal University of Maranhão - UFMA, Applied Computing Group - NCA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, São Luís, MA, 65085-580, Brazil.
authors.forename: Aristófanes Corrêa
authors.initials: AC
authors.lastname: Silva
authors.affiliation: Federal University of Maranhão - UFMA, Applied Computing Group - NCA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, São Luís, MA, 65085-580, Brazil.
authors.forename: Anselmo Cardoso
authors.initials: AC
authors.lastname: de Paiva
authors.affiliation: State University of Rio de Janeiro, Sao Francisco de Xavier, 524, Maracana, Rio de Janeiro, RJ, 20550-900, Brazil.
authors.forename: Rodolfo Acatauassú
authors.initials: RA
authors.lastname: Nunes
authors.affiliation: Department of Computer Science, Pontifical Catholic University of Rio de Janeiro - PUC-Rio, R. Marquês de São Vicente, 225, Gávea, Rio de Janeiro, RJ, 22453-900, Brazil.
authors.forename: Marcelo
authors.initials: M
authors.lastname: Gattass
mesh_terms: D000465:Algorithms; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging* / Q000473:pathology*; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Cylinder-based analysis; Lung cancer; Medical image; Shape diagrams