Chronic Obstructive Pulmonary Disease (COPD) is major cause of death and disability world-wide. It affects lung function through destruction of lung tissue known as emphysema and inflammation of airways, leading to thickened airway walls and narrowed airway lumen. Computed Tomography (CT) imaging have become the standard with which to assess emphysema extent but airway abnormalities have so far been more challenging to quantify. Automated methods for analysis are indispensable as the visible airway tree in a CT scan can include several hundreds of individual branches. However, automation is difficult, due to the airway’s complicated structure, which varies in size and shape; biologically between subjects and dynamically during breathing.

This thesis presents several methods for solving problems related to analyzing airways and results of using these methods to study COPD via data from the Danish Lung Cancer Screening Trial. This includes methods for extracting airway surfaces from the images and ways of achieving comparable measurements in airway branches through matching and anatomical labelling.

The methods were used to study effects of differences in inspiration level at the time of scan on airway dimensions in subjects with and without COPD. The results show measured airway dimensions to be affected by differences in the level of inspiration and this dependency is again influenced by COPD. Inspiration level should therefore be accounted for when measuring airways, and airway abnormalities typically associated with decreased lung function, should at least partly be understood as being due to differences in how airways are influenced by the inspiration level in subjects with and without COPD.