title: Quantitative assessment of the influence of anatomic noise on the detection of subtle lung nodule in digital chest radiography using fractal-feature distance.
abstract: OBJECTIVE
  To confirm whether or not the influence of anatomic noise on the detection of nodules in digital chest radiography can be evaluated by the fractal-feature distance.
  
  
  METHODS
  We used the square images with and without a simulated nodule which were generated in our previous observer performance study; the simulated nodule was located on the upper margin of a rib, the inside of a rib, the lower margin of a rib, or the central region between two adjoining ribs. For the square chest images, fractal analysis was conducted using the virtual volume method. The fractal-feature distances between the considered and the reference images were calculated using the pseudo-fractal dimension and complexity, and the square images without the simulated nodule were employed as the reference images. We compared the fractal-feature distances with the observer's confidence level regarding the presence of a nodule in plain chest radiograph.
  
  
  RESULTS
  For all square chest images, the relationships between the length of the square boxes and the mean of the virtual volumes were linear on a log-log scale. For all types of the simulated nodules, the fractal-feature distance was the highest for the simulated nodules located on the central region between two adjoining ribs and was the lowest for those located in the inside of a rib. The fractal-feature distance showed a linear relation to an observer's confidence level.
  
  
  CONCLUSIONS
  The fractal-feature distance would be useful for evaluating the influence of anatomic noise on the detection of nodules in digital chest radiography.
authors.affiliation: Department of Radiological Technology, Nagoya University School of Health Sciences, 1-20 Daikominami 1-chome, Higashi-ku, Nagoya 461-8673, Japan. imai@met.nagoya-u.ac.jp
authors.forename: Kuniharu
authors.initials: K
authors.lastname: Imai
authors.forename: Mitsuru
authors.initials: M
authors.lastname: Ikeda
authors.forename: Yukihiro
authors.initials: Y
authors.lastname: Enchi
authors.forename: Takanaga
authors.initials: T
authors.lastname: Niimi
mesh_terms: D016477:Artifacts; D017709:Fractals; D006801:Humans; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*