Remarkable advances in computed tomography (CT) technology geared our research toward investigating the integrative function of the lung and the development of a database of the airway tree incorporating anatomical and functional data with computational models. As part of this project, we are developing the algorithm to construct an anatomically realistic geometric model of airways from CT images. The basic concept of the algorithm is to segment airway trees from CT images as many as possible and later correct quantified parameters based on CT values. CT images are acquired with a 64-channel multidetector CT, and the airway is then extracted from CT images by the region-growing method while maintaining connectivity. Using this method, 428 airways up to the 14th branching generations were extracted. Although the airway diameters up to the 4th generation showed good agreement with those reported in an autopsy study, the diameters in later generations were all greater than the reported values due to the limited resolution of the CT images. The errors in diameter were corrected by assessing the relationship between diameter and median value of Hounsfield unit (HU) intensity of each airway; consequently, the diameters up to generation 8 agreed well with the reported values. Based on these results, we conclude that the use of HU-based correction algorithm combined with rough segmentation can be another way to improve data accuracy in the morphometric analysis of airways from CTs.