Chronic airway disease causes structural changes in the lungs including peribronchial thickening and airway dilatation.
Multi-detector computed tomography (CT) yields detailed near-isotropic images of the lungs, and thus the potential to
obtain quantitative measurements of lumen diameter and airway wall thickness. Such measurements would allow
standardized assessment, and physicians to diagnose and locate airway abnormalities, adapt treatment, and monitor
progress over time. However, due to the sheer number of airways per patient, systematic analysis is infeasible in routine
clinical practice without automation. We have developed an automated and real-time method based on active contours to
estimate both airway lumen and wall dimensions; the method does not require manual contour initialization but only a
starting point on the targeted airway. While the lumen contour segmentation is purely region-based, the estimation of the
outer diameter considers the inner wall segmentation as well as local intensity variation, in order anticipate the presence
of nearby arteries and exclude them. These properties make the method more robust than the Full-Width Half Maximum
(FWHM) approach. Results are demonstrated on a phantom dataset with known dimensions and on a human dataset
where the automated measurements are compared against two human operators. The average error on the phantom
measurements was 0.10mm and 0.14mm for inner and outer diameters, showing sub-voxel accuracy. Similarly, the mean
variation from the average manual measurement was 0.14mm and 0.18mm for inner and outer diameters respectively.