A novel method for automated segmentation of airway tree

Accurate and automatic segmentation of airway tree from multi-slice computed tomography(MSCT) chest scan is an essential step for automatic computer aided diagnosing pulmonary diseases, including pulmonary emboli, pulmonary function and nodules detection. Due to the low contrast between airway walls and lung tissue on CT values, providing an accurate and in-vivo segmentation method for reconstructing of 3D anatomical tree structures from MSCT chest scan is a challenging issue for computer vision in medical imaging. In this paper a new-fully automated approach to segmentation airway tree is proposed. Firstly, the 3D seed point is extracted using the adaptive threshold algorithm in the first slice image, which the bronchi is demonstrated in the image. Secondly, the segmentation main bronchi with 3D region growing from the seed point and detection leaking into the lung parenchyma with computing the intergenerational volume are simultaneous computed. Thirdly, the probable leaking points are selected using simulating 3D region growing based on parallel computing when the leakage is detected. Finally, the segmentation bronchi is recycled with the selected seed points until up to the stopping condition. The results show that the proposed approach provides an automatic and efficient method to extract airway tree.