title: Lung nodule segmentation in chest computed tomography using a novel background estimation method.
abstract: BACKGROUND
  Lung cancer results in the highest number of cancer deaths worldwide. The segmentation of lung nodules is an important task in computer systems to help physicians differentiate malignant lesions from benign lesions. However, it has already been observed that this may be a difficult task, especially when nodules are connected to an anatomical structure.
  
  
  METHODS
  This paper proposes a method to estimate the background of the nodule area and how this estimation is used to facilitate the segmentation task.
  
  
  RESULTS
  Our experiments indicate more than 99% of accuracy with less than 1% of false positive rate (FPR).
  
  
  CONCLUSIONS
  The proposed methods achieved better results than a state-of-the-art approach, indicating potential to be used in medical image processing systems.
authors.affiliation: 1 Department of Informatics, Federal University of Technology-Paraná, Via do Conhecimento Km 1, Pato Branco, PR, Brazil ; 2 Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada ; 3 Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil ; 4 Department of Radiology, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada ; 5 Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
authors.forename: Pablo G
authors.initials: PG
authors.lastname: Cavalcanti
authors.affiliation: 1 Department of Informatics, Federal University of Technology-Paraná, Via do Conhecimento Km 1, Pato Branco, PR, Brazil ; 2 Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada ; 3 Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil ; 4 Department of Radiology, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada ; 5 Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
authors.forename: Shahram
authors.initials: S
authors.lastname: Shirani
authors.affiliation: 1 Department of Informatics, Federal University of Technology-Paraná, Via do Conhecimento Km 1, Pato Branco, PR, Brazil ; 2 Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada ; 3 Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil ; 4 Department of Radiology, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada ; 5 Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
authors.forename: Jacob
authors.initials: J
authors.lastname: Scharcanski
authors.affiliation: 1 Department of Informatics, Federal University of Technology-Paraná, Via do Conhecimento Km 1, Pato Branco, PR, Brazil ; 2 Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada ; 3 Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil ; 4 Department of Radiology, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada ; 5 Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
authors.forename: Crystal
authors.initials: C
authors.lastname: Fong
authors.affiliation: 1 Department of Informatics, Federal University of Technology-Paraná, Via do Conhecimento Km 1, Pato Branco, PR, Brazil ; 2 Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada ; 3 Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil ; 4 Department of Radiology, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada ; 5 Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
authors.forename: Jane
authors.initials: J
authors.lastname: Meng
authors.affiliation: 1 Department of Informatics, Federal University of Technology-Paraná, Via do Conhecimento Km 1, Pato Branco, PR, Brazil ; 2 Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada ; 3 Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil ; 4 Department of Radiology, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada ; 5 Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
authors.forename: Jane
authors.initials: J
authors.lastname: Castelli
authors.affiliation: 1 Department of Informatics, Federal University of Technology-Paraná, Via do Conhecimento Km 1, Pato Branco, PR, Brazil ; 2 Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada ; 3 Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil ; 4 Department of Radiology, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada ; 5 Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
authors.forename: David
authors.initials: D
authors.lastname: Koff
keywords: Lung nodules; background estimation; chest CT; segmentation