Extraction of samples from airway and vessel trees in 3D lung CT based on a multi-scale principal curve tracing algorithm

The extraction of airway and vessel trees plays an important role in the diagnosis and treatment planning of lung diseases. However, this is a challenging task due to the small size of the anatomical structures, noise, or artifacts in the image. The similar intensity values between the lung parenchyma and airway lumen, the airway wall and the blood vessels make extraction particularly difficult. Our method detailed herein presents an automatic extraction of samples of both the airways and vessels from the three-dimensional computed tomography (3D-CT) based on the multi-scale principal curve algorithm. The image is first thresholded to find airway or vessel candidates according to their corresponding Hounsfield units (HU). The Frangi filter is then used to extract the tubular structures and remove background noise. Finally, a multi-scale principal curve projection and tracing algorithm is applied on the filtered image to identify the centerlines of the airway and vessel trees.