False-positive reduction using Hessian features in computer-aided detection of pulmonary nodules on thoracic CT images

Abstract

We are developing a computer-aided detection (CAD) system for lung nodules in thoracic CT volumes. During false positive (FP) reduction, the image structures around the identified nodule candidates play an important role in differentiating nodules from vessels. In our previous work, we exploited shape and first-order derivative information of the images by extracting ellipsoid and gradient field features. The purpose of this study was to explore the object shape information using second-order derivatives and the Hessian matrix to further improve the performance of our detection system. Eight features related to the eigenvalues of the Hessian matrix were extracted from a volume of interest containing the object, and were combined with ellipsoid and gradient field features to discriminate nodules from FPs. A data set of 82 CT scans from 56 patients was used to evaluate the usefulness of the FP reduction technique. The classification accuracy was assessed using the area A z under the receiving operating characteristic curve and the number of FPs per section at 80% sensitivity. In the combined feature space, we obtained a test A z of 0.97 ± 0.01, and 0.27 FPs/section at 80% sensitivity. Our results indicate that combining the Hessian, ellipsoid and gradient field features can significantly improve the performance of our FP reduction stage.