A Multi-Step Algorithm for Measuring Airway Luminal Diameter and Wall Thickness in Lung CT Images

Accurate measurements of airway diameter and wall thickness are important parameters in understanding numerous pulmonary diseases. Here, we describe an automated method of measuring small airway luminal diameter and wall thickness over numerous contiguous computed tomography (CT) images. Using CT lung images from 22 patients and an airway phantom, a seeded region-growing algorithm was first applied to identify the lumen of the airway. The result was applied as an initial region for boundary determination using the level set method. Once found, subsequent algorithmic expansion of the luminal border was used to calculate airway wall thickness. This algorithm automatically evaluates neighboring slices of the airway and measures the airway luminal diameter and wall thickness. This approach also detects airway bifurcations. Our new procedure provides rapid, automated, accurate, and clinically important lung airway measurements that would be useful to radiologists who use CT images for pulmonary disease assessment.