title: Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index.
abstract: OBJECTIVE
  The present work has the objective of developing an automatic methodology for the detection of lung nodules.
  
  
  METHODS
  The proposed methodology is based on image processing and pattern recognition techniques and can be summarized in three stages. In the first stage, the extraction and reconstruction of the pulmonary parenchyma is carried out and then enhanced to highlight its structures. In the second stage, nodule candidates are segmented. Finally, in the third stage, shape and texture features are extracted, selected and then classified using a support vector machine.
  
  
  RESULTS
  In the testing stage, with 140 new exams from the Lung Image Database Consortium image collection, 80% of which are for training and 20% are for testing, good results were achieved, as indicated by a sensitivity of 85.91%, a specificity of 97.70% and an accuracy of 97.55%, with a false positive rate of 1.82 per exam and 0.008 per slice and an area under the free response operating characteristic of 0.8062.
  
  
  CONCLUSIONS
  Lung cancer presents the highest mortality rate in addition to one of the smallest survival rates after diagnosis. An early diagnosis considerably increases the survival chance of patients. The methodology proposed herein contributes to this diagnosis by being a useful tool for specialists who are attempting to detect nodules.
authors: Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580 São Luís, MA, Brazil. Electronic address: antoniooseas@gmail.com.
authors: Antonio Oseas
authors: AO
authors: de Carvalho Filho
authors: Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580 São Luís, MA, Brazil. Electronic address: wenersampaio@gmail.com.
authors: Wener Borges
authors: WB
authors: de Sampaio
authors: Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580 São Luís, MA, Brazil. Electronic address: ari@dee.ufma.br.
authors: Aristófanes Corrêa
authors: AC
authors: Silva
authors: Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580 São Luís, MA, Brazil. Electronic address: paiva@deinf.ufma.br.
authors: Anselmo Cardoso
authors: AC
authors: de Paiva
authors: State University of Rio de Janeiro, São Francisco de Xavier, 524, Maracanã, 20550-900 Rio de Janeiro, RJ, Brazil. Electronic address: rodolfoacatauassu@yahoo.com.br.
authors: Rodolfo Acatauassú
authors: RA
authors: Nunes
authors: Pontifical Catholic University of Rio de Janeiro, R. São Vicente, 225, Gávea, 22453-900 Rio de Janeiro, RJ, Brazil. Electronic address: mgattass@tecgraf.puc-rio.br.
authors: Marcelo
authors: M
authors: Gattass
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008168:Lung / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000175:diagnosis*; D010363:Pattern Recognition, Automated; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000175:diagnosis*; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Computer-aided detection; Genetic algorithm; Lung cancer; Medical image; Nodule detection; Quality threshold; Support vector machine