Projection profile analysis for automated detection of abnormalities in chest radiographs

Abnormalities in chest images often present as abnormal opacity or abnormal asymmetry. We have developed a novel method for automated detection of abnormalities in chest radiographs by use of these features. Our method is based on an analysis of the projection profile obtained by projecting the pixels data of a frontal chest image on to the mediolateral axis. Two indices, lung opacity index and lung symmetry index, are computed from the projection profile. Lung opacity index and lung symmetry index are then combined to detect gross abnormalities in chest radiographs. The values of lung opacity index are found to be 0.38 +/- 0.05 and 0.37 +/- 0.06 for normal right and left lung, respectively. The values of lung symmetry index are found to be 0.018 +/- 0.014 for normal chest images. The discrimination for the combination of the two indices is evaluated by linear discriminant analysis and receiver operating characteristic (ROC) analysis. Area Az under the ROC curve with the combination of the two indices in the classification of normal and abnormal chest images is 0.963.