3D human airway segmentation for virtual bronchoscopy

This paper describes a new airway segmentation algorithm that improves the speed of morphological-based segmentation approaches. Airway segmentation methods based on morphological operators suffer from the indiscriminant application of all operators to a large area. Using the results of three-dimensional (3D) region growing, the discrete application of larger operators is possible. This change can greatly decrease the execution time of the algorithm. This hybrid approach typically runs 5 to 10 times faster than the original algorithm. 3D adaptive region growing, morphological segmentation, and the hybrid approach are then compared via data obtained from human volunteers using a Marconi MX8000 scanner with the lungs held at 85% TLC. Results show that filtering improves robustness of these techniques. The hybrid approach allows for the practical use of morphological operators to create a clinically useful segmentation. We also demonstrate the method's utility for peripheral nodule analysis in a human case.