Title: Airway segmentation from 3D chest CT volumes based on volume of interest using gradient vector flow,
Abstract: Some lung diseases are related to bronchial airway structures and morphology.
Although airway segmentation from chest CT volumes is an important task in the
computer-aided diagnosis and surgery assistance systems for the chest, complete
3-D airway structure segmentation is a quite challenging task due to its
complex tree-like structure. In this paper, we propose a new airway
segmentation method from 3D chest CT volumes based on volume of interests (VOI)
using gradient vector flow (GVF). This method segments the bronchial regions by
applying the cavity enhancement filter (CEF) to trace the bronchial tree
structure from the trachea. It uses the CEF in the VOI to segment each branch.
And a tube-likeness function based on GVF and the GVF magnitude map in each VOI
are utilized to assist predicting the positions and directions of child
branches. By calculating the tube-likeness function based on GVF and the GVF
magnitude map, the airway-like candidate structures are identified and their
centrelines are extracted. Based on the extracted centrelines, we can detect
the branch points of the bifurcations and directions of the airway branches in
the next level. At the same time, a leakage detection is performed to avoid the
leakage by analysing the pixel information and the shape information of airway
candidate regions extracted in the VOI. Finally, we unify all of the extracted
bronchial regions to form an integrated airway tree. Preliminary experiments
using four cases of chest CT volumes demonstrated that the proposed method can
extract more bronchial branches in comparison with other methods.