Automatic 3D segmentation of human airway tree in CT image

With the dawn of modern imaging technologies such as CT, MRI and PET, images play a role of ever increasing importance. Due to the high contrast between air and tissue, X-rays are the imaging modality of choice. Especially CT-scans are increasingly useful for diagnosis of disorders of the lung. Working with the acquired 3D CT data brings new difficulties as it is not trivial to display 3D data on a 2D monitor. One way to display this information is by reconstructing the structures and applying volume rendering on the segmented volumes. In this paper a novel method is presented for the segmentation of the airway tree. The proposed algorithm employs region growing, 3D wave propagation and morphological refinement to segment bronchi. The algorithm has been tested on 24 datasets resulting in airway trees that are successfully segmented up to the sixth generation, while execution times are as low as 2 seconds per airway tree.