IEEE transactions on medical imaging
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IEEE Trans Med Imaging · Feb 2014
Separation of bones from chest radiographs by means of anatomically specific multiple massive-training ANNs combined with total variation minimization smoothing.
Most lung nodules that are missed by radiologists as well as computer-aided detection (CADe) schemes overlap with ribs or clavicles in chest radiographs (CXRs). The purpose of this study was to separate bony structures such as ribs and clavicles from soft tissue in CXRs. To achieve this, we developed anatomically specific multiple massive-training artificial neural networks (MTANNs) combined with total variation (TV) minimization smoothing and a histogram-matching-based consistency improvement method. ⋯ This new method was compared with conventional MTANNs with a database of 110 CXRs with nodules. Our new anatomically specific MTANNs separated rib edges, ribs close to the lung wall, and the clavicles from soft tissue in CXRs to a substantially higher level than did the conventional MTANNs, while the conspicuity of lung nodules and vessels was maintained. Thus, our technique for bone-soft-tissue separation by means of our new MTANNs would be potentially useful for radiologists as well as CADe schemes in detection of lung nodules on CXRs.