Asthma and COPD are complex airway diseases with an increased incidence estimated for the next decade. Today, the mechanisms and relationships between airway structure/physiology and the clinical phenotype and genotype are not completely understood. We thus lack the tools to predict disease progression or therapeutic responses. One of the main causes is our limited ability to assess the complexity of airway diseases in large populations of patients with appropriate controls. Multi-slice computed tomography (MSCT) imaging opened the way to the non-invasive assessment of airway physiology and structure, but the use of such technology in large cohorts requires a high degree of automation of the measurements. This paper develops an investigation framework and the associated image quantification tools for high-throughput analysis of airways in MSCT. A mixed approach is proposed, combining 3D and cross-section measurements of the airway tree where the user-interaction is limited to the choice of the desired analysis patterns. Such approach relies on the fully-automated segmentation of the 3D airway tree, caliber estimation and visualization based on morphologic granulometry, central axis computation and tree segment selection, cross-section morphometry of airway lumen and wall, and bronchus longitudinal shape analysis for stenosis/bronciectasis detection and measure validation. The developed methodology has been successfully applied to a cohort of 96 patients from a multi-center clinical study of asthma control in moderate and persistent asthma.