title: Fractal analysis of interstitial lung abnormalities in chest radiography.
abstract: A computerized method for analyzing interstitial lung abnormalities seen on chest radiographs was investigated. The method includes two main steps: (a) extraction of linear opacities on chest radiographs and (b) calculation of the fractal dimension. Extraction of linear opacities uses the processes of four-directional Laplacian-Gaussian filtering, binarization, and linear opacity judgment. The fractal dimensions in the processed images are then calculated by using the box-counting algorithm. The accuracy of the computerized method in differentiating between normal and abnormal lung tissue was tested on digitized chest radiographs (0.175 mm pixel, 10-bit) of 100 randomly selected patients. One hundred regions of interest (ROIs) from radiographs of 50 patients with interstitial lung abnormalities and 100 ROIs from radiographs of 50 patients with normal lungs were analyzed. The fractal dimensions obtained from the ROIs in lungs with interstitial abnormalities were significantly higher compared with those from ROIs in normal lungs (mean, 1.67 +/- 0.10 vs 1.44 +/- 0.12, respectively; P < .001). This result indicates that fractal analysis is useful in distinguishing interstitial lung abnormalities from normal lung tissue on chest radiographs.
authors.affiliation: Department of Radiology, Nishinomiya Municipal Central Hospital, Hyogo, Japan.
authors.forename: S
authors.initials: S
authors.lastname: Kido
authors.forename: J
authors.initials: J
authors.lastname: Ikezoe
authors.forename: H
authors.initials: H
authors.lastname: Naito
authors.forename: S
authors.initials: S
authors.lastname: Tamura
authors.forename: S
authors.initials: S
authors.lastname: Machi
mesh_terms: D000328:Adult; D005260:Female; D017709:Fractals; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D017563:Lung Diseases, Interstitial / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D011857:Radiographic Image Interpretation, Computer-Assisted