RULE-BASED DETECTION OF INTRATHORACIC AIRWAY TREES

New sensitive and reliable methods for assessing alterations in region al lung structure and function are critically important for the invest igation and treatment of pulmonary diseases, Accurate identification o f the airway tree will provide an assessment of airway structure and w ill provide a means by which multiple volumetric images of the lung at the same lung volume over time can be used to assess regional parench ymal changes, We describe a novel rule-based method for the segmentati on of airway trees from three-dimensional (3-D) sets of computed tomog raphy (CT) images, and its validation, The presented method takes adva ntage of a priori anatomical knowledge about pulmonary airway and vasc ular trees and their interrelationships, The method is based on a comb ination of 3-D seeded region growing that is used to identify large ai rways, rule-based two-dimensional (2-D) segmentation of individual CT slices to identify probable locations of smaller diameter airways, and merging of airway regions across the 3-D set of slices resulting in a tree-like airway structure, The method was validated in 40 3-mm-thick CT sections from five data sets of canine lungs scanned via electron beam CT in vivo with lung volume held at a constant pressure, The meth od's performance was compared with that of the conventional 3-D region growing method, The method substantially outperformed an existing con ventional approach to airway tree detection.