Automatic 3D Segmentation of Lung Airway Tree: A Novel Adaptive Region Growing Approach

In diagnosing pulmonary diseases aided by computer, accurate segmentation of the airway tree from the CT images is the basis for subsequent processing and analyzing. It is still a challenging task due to the image noise, partial volume effect and texture similarity of the airway and parenchyma. In order to solve these problems, various algorithms have been proposed, among which the region growing is the most commonly used one. However, previous region growing algorithms, either those using constant parameters or those using adaptive parameters, suffered from leakage and/or disconnection. This paper presents a novel adaptive region growing approach using two-step processing. The first step is rough segmentation, for dividing the sub-volumes surrounding the airway into three types according to their topology; and the second step is fine segmentation, using specific methods for each type. The experimental results show that the proposed approach can effectively suppress leakage and remedy disconnection.