Abstract : Feature-based registration methods offer a robust alternative to intensity-based methods when intensities change because of pathology, image artifacts or differences in acquisition. For registration of lung CT images, we propose to use distinctive anatomical structures, such as the pulmonary vessel tree centerlines and lung surfaces, to establish correspondences between pairs of scans. In this respect, we develop and evaluate a curve- and surface-based registration method using currents. This method does not require point correspondence between structures. We conducted experiments on five pairs of images, where each pair consists of image volumes extracted at the end inhale and end exhale phases of a 4D-CT scan. To evaluate the registration, we used a set of 300 anatomical landmarks marked on each image pair. Using both vessel centerlines curves and lung surfaces yields better alignment (median error of 1.85 mm) than using only curves (2.37 mm) or surfaces (3.53 mm). The combined method achieves overall registration accuracy comparable to that of intensity-based registration, whereas the errors are made in different locations. This suggests that low dimensional geometrical features capture sufficient information to drive a reliable registration, while results can still be improved by combining intensity and feature based registration approaches into one framework
