The segmentation of pulmonary airway tree serves an important function in the accurate localization and quantitative evaluation of lung diseases. Considering the size

shape

and density variations among different bronchus branches

we propose a Method for 3D pulmonary airway tree segmentation by combining region growing and morphological grayscale reconstruction algorithms with a focus on solvinglocal leakage problems. First

the region of interestor maskis defined by extracting the lung parenchyma with a global threshold and morphological closingoperation.The trachea and large bronchi are then segmented by using an improved region growing Method based on the iterative hysteresis threshold

and a local volume explosion index is adopted to suppress the lateral leakage. The smaller bronchi are segmented using a 3D morphological grayscale reconstruction algorithm

and a connected component-based shape descriptor is defined to remove the pseudo-tracheal regions. Finally

a complete airway tree is obtained by integrating the segmentation Results from the initial two steps. The algorithms are tested by using the publicly available data of the EXACT'09 challenge

and quantitative evaluation is conducted with the bifurcation number

branch number

and branch ratio o fthe 20 test CT cases by comparing the details with a manual reference. Results show that the proposed Method can identify more than half of the branches in the reference

and the mean number of detected branches can reach 55.5% under a relatively low leakage rate. Compared with that of other considered Methods

the performance indexes of the proposed Method are moderately better. Although the algorithm appears to be simple and has low computational complexity

the proposed Method effectively prevents leakage.