Improved computerized detection of lung nodules in chest radiographs by means of 'virtual dual-energy' radiography

Abstract

Major challenges in current computer-aided detection (CADe) of nodules in chest radiographs (CXRs) are to detect nodules that overlap with ribs and to reduce the frequent false positives (FPs) caused by ribs. Our purpose was to develop a CADe scheme with improved sensitivity and specificity by use of virtual dual-energy (VDE) CXRs where ribs are suppressed with a massive-training artificial neural network (MTANN). To reduce rib-induced FPs and detect nodules overlapping with ribs, we incorporated VDE technology in our CADe scheme. VDE technology suppressed ribs in CXR while maintaining soft-tissue opacity by use of an MTANN that had been trained with real DE imaging. Our scheme detected nodule candidates on VDE images by use of a morphologic filtering technique. Sixty-four morphologic and gray-level-based features were extracted from each candidate from both original and VDE CXRs. A nonlinear support vector classifier was employed for classification of the nodule candidates. A publicly available database containing 126 nodules in 126 CXRs was used for testing of our CADe scheme. Twenty nine percent (36/126) of the nodules were rated extremely subtle or very subtle by a radiologist. With the original scheme, a sensitivity of 76.2 (96/126) with 5 (630/126) FPs per image was achieved. By use of VDE images, more nodules overlapping with ribs were detected and the sensitivity was improved substantially to 84.1% (106/126) at the same FP rate in a leave-one-out cross-validation test, whereas the literature shows that other CADe schemes achieved sensitivities of 66.0% and 72.0% at the same FP rate.