Automated localized approach for airway segmentation in 3D chest CT volume

Abstract

Bronchial airway structure and morphology identification is very useful for analysis of many lung diseases. Since, the human tracheo-bronchial tree is a dyadic non-symmetric branching network which is very complex and its manual tracing is quite tedious and unwieldy. Moreover, automatic detection techniques for airway are quite challenging. This is due to its complexity and fading off the airway intensity because of the smaller asynchronous branching and noise in the image reconstruction. In this paper, an unsupervised approach for segmentation of localized airway has been proposed after segmenting the lung region. Firstly, airways are segmented out by using 3D region growing techniques with intensity constrained to prevent leakages. This results in limited segmentation of airways due to partial volume effect and leakage risk. Further, deeper bronchial branches are segmented by applying adaptive morphological techniques on 3D segmented lungs. Then, these two results are combined followed by 3D region growing to get complete segmentation of airway. The proposed technique is tested on Exact'09 20 test cases and evaluated by Exact'09 team. The performance of the proposed approach is quite reliable in segmenting distal branches with reasonable leakages. The advantage of this scheme is that it is easy to implement, fully automated, and time efficient.