title: Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography.
abstract: An automated computerized scheme has been developed for the detection and characterization of diffuse lung diseases on high-resolution computed tomography (HRCT) images. Our database consisted of 315 HRCT images selected from 105 patients, which included normal and abnormal slices related to six different patterns, i.e., ground-glass opacities, reticular and linear opacities, nodular opacities, honeycombing, emphysematous change, and consolidation. The areas that included specific diffuse patterns in 315 HRCT images were marked by three radiologists independently on the CRT monitor in the same manner as they commonly describe in their radiologic reports. The areas with a specific pattern, which three radiologists marked independently and consistently as the same patterns, were used as "gold standard" for specific abnormal opacities in this study. The lungs were first segmented from the background in each slice by use of a morphological filter and a thresholding technique, and then divided into many contiguous regions of interest (ROIs) with a 32x32 matrix. Six physical measures which were determined in each ROI included the mean and the standard deviation of the CT value, air density components, nodular components, line components, and multilocular components. Artificial neural networks (ANNs) were employed for distinguishing between seven different patterns which included normals and six patterns associated with diffuse lung disease. The sensitivity of this computerized method for a detection of the six abnormal patterns in each ROI was 99.2% (122/123) for ground-glass opacities, 100% (15/15) for reticular and linear opacities, 88.0% (132/150) for nodular opacities, 100% (98/98) for honeycombing, 95.8% (369/385) for emphysematous change, and 100% (43/43) for consolidation. The specificity in detecting a normal ROI was 88.1% (940/1067). This computerized method may be useful in assisting radiologists in their assessment of diffuse lung disease in HRCT images.
authors.affiliation: Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, 5841 S. Maryland Ave., Chicago, Illinois 60637, USA. yoshi@apex.bsd.uchicago.edu
authors.forename: Yoshikazu
authors.initials: Y
authors.lastname: Uchiyama
authors.forename: Shigehiko
authors.initials: S
authors.lastname: Katsuragawa
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Abe
authors.forename: Junji
authors.initials: J
authors.lastname: Shiraishi
authors.forename: Feng
authors.initials: F
authors.lastname: Li
authors.forename: Qiang
authors.initials: Q
authors.lastname: Li
authors.forename: Chao-Tong
authors.initials: CT
authors.lastname: Zhang
authors.forename: Kenji
authors.initials: K
authors.lastname: Suzuki
authors.forename: Kunio
authors.initials: K
authors.lastname: Doi
mesh_terms: D000465:Algorithms; D003937:Diagnosis, Differential; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging; D008171:Lung Diseases / Q000145:classification* / Q000000981:diagnostic imaging*; D016571:Neural Networks, Computer; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D011786:Quality Control; D012372:ROC Curve; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*