Assessing the airway wall thickness in multi slice computed tomography (MSCT) as image marker for airway disease phenotyping such asthma and COPD is a current trend and challenge for the scientific community working in lung imaging. This paper addresses the same problem from a different point of view: considering the expected wall thickness-to-lumen-radius ratio for a normal subject as known and constant throughout the whole airway tree, the aim is to build up a 3D map of airway wall regions of larger thickness and to define an overall score able to highlight a pathological status. In this respect, the local dimension (caliber) of the previously segmented airway lumen is obtained on each point by exploiting the granulometry morphological operator. A level set function is defined based on this caliber information and on the expected wall thickness ratio, which allows obtaining a good estimate of the airway wall throughout all segmented lumen generations. Next, the vascular (or mediastinal dense tissue) contact regions are automatically detected and excluded from analysis. For the remaining airway wall border points, the real wall thickness is estimated based on the tissue density analysis in the airway radial direction; thick wall points are highlighted on a 3D representation of the airways and several quantification scores are defined. The proposed approach is fully automatic and was evaluated (proof of concept) on a patient selection coming from different databases including mild, severe asthmatics and normal cases. This preliminary evaluation confirms the discriminative power of the proposed approach regarding different phenotypes and is currently extending to larger cohorts.