title: Zone-based analysis for automated detection of abnormalities in chest radiographs.
abstract: OBJECTIVE
  The aim of this study was to develop an automated method for detection of local texture-based and density-based abnormalities in chest radiographs.
  
  
  METHODS
  The method was based on profile analysis to detect abnormalities in chest radiographs. In the method, one density-based feature, Density Symmetry Index, and two texture-based features, Roughness Maximum Index and Roughness Symmetry Index, were used to detect abnormalities in the lung fields. In each chest radiograph, the lung fields were divided into four zones initially and then the method was applied to each zone separately. For each zone, Density Symmetry Index was obtained from the projection profile of each zone, and Roughness Maximum Index and Roughness Symmetry Index were obtained by measuring the roughness of the horizontal profiles via moving average technique. Linear discriminant analysis was used to classify normal and abnormal cases based on the three indices. The discriminant performance of the method was evaluated using ROC analysis.
  
  
  RESULTS
  The method was evaluated on a database of 250 normal and 250 abnormal chest images. In the optimized conditions, the zone-based performance Az of the method for zones 1, 2, 3, and 4 were 0.917, 0.897, 0.892, and 0.814, respectively, and the case-based performance Az of the method was 0.842. Our previous method for detection of gross abnormalities was also evaluated on the same database. The case-based performance of our previous method was 0.689.
  
  
  CONCLUSIONS
  In comparing the previous method and the new method proposed in this study, there was a great improvement by the new method for detection of local texture-based and density-based abnormalities. The new method combined with the previous one has potential for screening abnormalities in chest radiographs.
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: Yu-Ting
authors.initials: YT
authors.lastname: Kuo
authors.forename: Jui-Sheng
authors.initials: JS
authors.lastname: Hsu
authors.forename: Ming-Chung
authors.initials: MC
authors.lastname: Chou
authors.forename: Gin-Chung
authors.initials: GC
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D006801:Humans; D008171:Lung Diseases / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity