title: Automated patient identity recognition by analysis of chest radiograph features.
abstract: OBJECTIVE
  The aim of this study was to develop a computerized scheme for automated identity recognition based on chest radiograph features.
  
  
  METHODS
  The proposed method was evaluated on a database consisting of 1000 pairs of posteroanterior chest radiographs. The method was based on six features: length of the lung field, size of the heart, area of the body, and widths of the upper, middle, and lower thoracic cage. The values for the six features were determined from a chest image, and absolute differences in feature values between the two images (feature errors) were used as indices of image similarity. The performance of the proposed method was evaluated by receiver operating characteristic (ROC) analysis. The discriminant performance was evaluated as the area Az under the ROC curve.
  
  
  RESULTS
  The discriminant performance Az of the feature errors for lung field length, heart size, body area, upper cage width, middle cage width, and lower cage width were 0.794 ± 0.005, 0.737 ± 0.007, 0.820 ± 0.008, 0.860 ± 0.005, 0.894 ± 0.006, and 0.873 ± 0.006, respectively. The combination of the six feature errors obtained an Az value of 0.963 ± 0.002.
  
  
  CONCLUSIONS
  The results indicate that combining the six features yields a high discriminant performance in recognizing patient identity. The method has potential usefulness for automated identity recognition to ensure that chest radiographs are associated with the correct patient.
authors.affiliation: Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan. tonykao@kmu.edu.tw
authors.forename: E-Fong
authors.initials: EF
authors.lastname: Kao
authors.forename: Wei-Chen
authors.initials: WC
authors.lastname: Lin
authors.forename: Twei-Shiun
authors.initials: TS
authors.lastname: Jaw
authors.forename: Gin-Chung
authors.initials: GC
authors.lastname: Liu
authors.forename: Jain-Shing
authors.initials: JS
authors.lastname: Wu
authors.forename: Chung-Nan
authors.initials: CN
authors.lastname: Lee
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010355:Patient Identification Systems / Q000379:methods* / Q000706:statistics & numerical data*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods* / Q000706:statistics & numerical data*; D011873:Radiology Information Systems / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult
keywords: Identity recognition; chest radiograph; feature analysis