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Lung nodule detection in chest radiographs

In the last decades, several research projects have been devoted to the development of computer-aided diagnosis (CAD) systems that could help radiologists to detect lung nodules. Despite the great effort, the problem is still open. In this dissertation, a new CAD system is presented for the detection of lung nodules in posterior-anterior chest radiographs. The lung area considered in this work includes the regions obscured by the mediastinum, the diaphragm and other anatomic structures (hidden regions). Although those regions are usually excluded from the methods presented in the literature, they were included because some lung nodules may be found there. To detect nodules from both visible and hidden regions, we used a CAD system where these regions were assessed separately and the classification results combined in the end. In sum, after segmenting those lung regions, a multi-scale blob detector was used to nodule candidate detection. At the last stage, the Support Vector Machine (SVM) methodology was employed to candidate classification in the visible region where we obtained a sensitivity of 83% at 6.5 false positives (FP) per image (i.e., per patient) and 90% at 9 FP per image. For the hidden region, a specially conceived classifier – Hyperpolyhedron Classifier – was used, achieving a sensitivity of 82% at 1.8 FP per image.