title: Segmentation and quantitative analysis of intrathoracic airway trees from computed tomography images.
abstract: The segmentation of the human airway tree from volumetric multidetector-row computed tomography images is an important prerequisite for many clinical applications and physiologic studies. We present a new airway segmentation method based on fuzzy connectivity. Small adaptive regions of interest are used that follow the airway branches as they are segmented. This method works on various types of scans (low dose and regular dose, normal subjects and diseased subjects) without the need for the user to manually adjust any parameters. Comparison with a commonly used region-growing segmentation algorithm shows that this method retrieves a significantly higher count of airway branches. In an additional processing step, this method provides accurate cross-sectional airway measurements that are conducted in the original gray-level volume. Validation on a phantom shows that subvoxel accuracy is achieved for all airway sizes and airway orientations. The utility of the reported method is demonstrated in a comparative analysis of normal and cystic fibrosis airway trees.
authors.affiliation: Department of Electrical and Computer Engineering, 4016 SC, University of Iowa, Iowa City, IA 52242, USA.
authors.forename: Juerg
authors.initials: J
authors.lastname: Tschirren
authors.forename: Eric A
authors.initials: EA
authors.lastname: Hoffman
authors.forename: Geoffrey
authors.initials: G
authors.lastname: McLennan
authors.forename: Milan
authors.initials: M
authors.lastname: Sonka
mesh_terms: D000465:Algorithms; D003550:Cystic Fibrosis / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional; D008168:Lung / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*