title: Automated estimation of progression of interstitial lung disease in CT images.
abstract: OBJECTIVE
  A system is presented for automated estimation of progression of interstitial lung disease in serial thoracic CT scans.
  
  
  METHODS
  The system compares corresponding 2D axial sections from baseline and follow-up scans and concludes whether this pair of sections represents regression, progression, or unchanged disease status. The correspondence between serial CT scans is achieved by intrapatient volumetric image registration. The system classification function is trained with two different feature sets. Features in the first set represent the intensity distribution of a difference image between the baseline and follow-up CT sections. Features in the second set represent dissimilarities computed between the baseline and follow-up images filtered with a bank of general purpose texture filters.
  
  
  RESULTS
  In an experiment on 74 scan pairs, the system classification accuracies were 76.1% and 79.5% for the two feature sets, respectively, while the accuracies of two observer radiologist were 78.5% and 82%, respectively. The agreements of the system with the reference standard, measured by weighted kappa statistics, were 0.611 and 0.683 for the two feature sets, respectively.
  
  
  CONCLUSIONS
  The system employing the second feature set showed good agreement with the reference standard, and its accuracy approached that of two radiologists.
authors.affiliation: CSIRO Mathematical and Infonnrmation Sciences, New South Wales 1670, Australia. yulia.arzhaeva@csiro.au
authors.forename: Yulia
authors.initials: Y
authors.lastname: Arzhaeva
authors.forename: Mathias
authors.initials: M
authors.lastname: Prokop
authors.forename: Keelin
authors.initials: K
authors.lastname: Murphy
authors.forename: Eva M
authors.initials: EM
authors.lastname: van Rikxoort
authors.forename: Pim A
authors.initials: PA
authors.lastname: de Jong
authors.forename: Hester A
authors.initials: HA
authors.lastname: Gietema
authors.forename: Max A
authors.initials: MA
authors.lastname: Viergever
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D017563:Lung Diseases, Interstitial / Q000000981:diagnostic imaging*; 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; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*