Standard moments based vessel bifurcation filter for computer-aided detection of pulmonary nodules

This work describes a method that can discriminate between a solid pulmonary nodule and a pulmonary vessel bifurcation point at a given candidate location on a CT scan using the method of standard moments. The algorithm starts with the estimation of a spherical window around a nodule candidate center that best captures the local shape properties of the region. Then, given this window, the standard set of moments, invariant to rotation and scale is computed over the geometric representation of the region. Finally, a feature vector composed of the moment values is classified as either a nodule or a vessel bifurcation point. The performance of this technique was evaluated on a dataset containing 276 intraparenchymal nodules and 276 selected vessel bifurcation points. The method resulted in 99% sensitivity and 80% specificity in identifying nodules, which makes this technique an efficient filter for false positives reduction. Its efficiency was further evaluated on the dataset of 656 low-dose chest CT scans. Inclusion of this filter into a design of an experimental detection system resulted in up to a 69% decrease in false positive rate in detection of intraparenchymal nodules with less than 1% loss in sensitivity.