Three-dimensional human airway segmentation methods for clinical virtual bronchoscopy.
OBJECTIVE
The segmentation of airways from CT images is a critical first step for numerous virtual bronchoscopic (VB) applications. Automatic or semiautomatic methods are necessary, since manual segmentation is prohibitively time consuming. The methods must be robust and operate within a reasonable time frame to be useful for clinical VB use. The authors developed an integrated airway segmentation system and demonstrated its effectiveness on a series of human images.


METHODS
The authors' airway segmentation system draws on two segmentation algorithms: (a) an adaptive region-growing algorithm and (b) a new hybrid algorithm that uses both region growing and mathematical morphology. Images from an ongoing VB study were segmented by means of both the adaptive region-growing and the new hybrid methods. The segmentation volume, branch number estimate, and segmentation quality were determined for each case.


RESULTS
The results demonstrate the need for an integrated segmentation system, since no single method is superior for all clinically relevant cases. The region-growing algorithm is the fastest and provides acceptable segmentations for most VB applications, but the hybrid method provides superior airway edge localization, making it better suited for quantitative applications. In addition, the authors show that prefiltering the image data before airway segmentation increases the robustness of both region-growing and hybrid methods.


CONCLUSIONS
The combination of these two algorithms with the prefiltering options allowed the successful segmentation of all test images. The times required for all segmentations were acceptable, and the results were suitable for the authors' VB application needs.
Kiraly
Atilla P
AP
Department of Computer Science and Engineering, Penn State University, PA, USA.
Higgins
William E
WE
McLennan
Geoffrey
G
Hoffman
Eric A
EA
Reinhardt
Joseph M
JM
D000465:Algorithms; D001999:Bronchoscopy; D003203:Computers, Hybrid; D005500:Follow-Up Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods; D019047:Phantoms, Imaging; D012140:Respiratory Tract Diseases / Q000175:diagnosis*; D014057:Tomography, X-Ray Computed / Q000295:instrumentation; D014584:User-Computer Interface