Automatic axis generation for 3D virtual-bronchoscopic image assessment

Virtual bronchoscopy is emerging as a means for assessing high-resolution 3D CT images of the chest. The central axes, or paths, of the airways can provide virtual-bronchoscopic systems with a logical reference frame for quantitation and navigation. Unfortunately, the manual and automatic methods proposed to date for determining these axes are either time-consuming, error prone, or provide imprecise results. We give a preliminary presentation of an adaptive automated approach for finding smooth central axes through the major airways. Using this method, we are able to extract multiple axes through a 3D image in only a few minutes for a typical 512 × 512 × 25 CT image. The method works on anisotropically sampled gray-scale images and requires no prior segmentation. We describe the method and present initial validation results for phantom, animal, and human images. Visual results are also provided using a virtual bronchoscopic system.