[Computer-aided diagnosis of interstitial lung diseases]

Nihon Igaku Hoshasen Gakkai Zasshi. 1990 Jul 25;50(7):753-66.
[Article in Japanese]


We are developing an automated method for determination of quantitative physical measures of lung textures in digital chest radiographs in order to detect and characterize interstitial lung disease. We describe a scheme of our approach for lung texture analysis, an automated classification method for distinction between normal and abnormal lungs with interstitial disease, and the effect of digital parameters on the accuracy of this computerized analysis, as well as applications of this method to the ILO pneumoconioses standard radiographs. The root-mean-square (rms) variation and the first moment of the power spectrum of the lung texture were determined as quantitative texture measures based on a frequently analysis of lung textures, which represent the magnitude and coarseness (or fineness) of the lung textures, respectively. The computerized classification method is based on the analysis of these texture measures and on a data base derived from clinical cases. This classification method includes three independent tests, one for a definitely abnormal focal pattern, one for a relatively localized abnormal pattern, and one for a diffuse abnormal pattern. A comparison of receiver operating characteristic (ROC) curves obtained by radiologists and by means of the computerized classification method indicates that the computerized approach may provide performance similar to human observers in distinguishing lungs with mild interstitial disease from normal lungs. By investigating the effect of digital parameters such as pixel size, ROI size and the number of quantization levels on these texture measures obtained from the lung texture analysis and the performance of this computerized method, we attained a useful guide in the design of this computerized scheme. Texture measures obtained from computer analysis of the ILO pneumoconioses standard radiographs corresponded closely with the ILO classification categories for small opacities, though it was necessary for a qualified radiologist to identify representative areas in each ILO radiograph because of the inhomogeneous distribution of texture patterns in these standard radiographs. Our results suggest strongly that this computerized method can be a valuable aid to radiologists in their assessment of interstitial lung diseases.

MeSH terms

  • Humans
  • Pulmonary Fibrosis / diagnostic imaging*
  • Radiographic Image Interpretation, Computer-Assisted*