Image feature analysis and computer-aided diagnosis in digital radiography: classification of normal and abnormal lungs with interstitial disease in chest images.
In order to detect and characterize interstitial disease in the lungs, we are developing an automated method for the determination of physical texture measures, which assess the magnitude and coarseness (or fineness) of lung texture in digital chest radiographs. This method is based on an analysis of the power spectrum of lung texture. We now describe an automated classification method for distinction between normal and abnormal lungs with interstitial disease, in which we employ these texture measures and their data base. This computerized 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. The performance of this computerized classification scheme is compared with that of radiologists by means of receiver operating characteristic (ROC) analysis. Our results indicate that this computerized method can be a valuable aid to radiologists in their assessment of interstitial infiltrates.
Katsuragawa
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S
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, Illinois 60637.
Doi
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K
MacMahon
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H
D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008168:Lung / Q000002:abnormalities* / Q000000981:diagnostic imaging; D011658:Pulmonary Fibrosis / Q000145:classification / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement