SEGMENTATION OF INTRATHORACIC AIRWAY TREES - A FUZZY-LOGIC APPROACH

Three-dimensional (3-D) analysis of airway trees extracted from comput ed tomography (CT) image data can provide objective information about lung structure and function, However, manual analysis of 3-D lung CT i mages is tedious, time consuming and, thus, impractical for routine cl inical care, We have previously reported an automated rule-based metho d for extraction of airway trees from 3-D CT images using a priori kno wledge about airway-tree anatomy. Although the method's sensitivity wa s quite good, its specificity suffered from a large number of falsely detected airways, Wc present a new approach to airway-tree detection b ased on fuzzy logic that increases the method's specificity without co mpromising its sensitivity. The method was validated in 32 CT image sl ices randomly selected from five volumetric canine electron-beam CT da ta sets, The fuzzy-logic method significantly outperformed the previou sly reported rule-based method (p < 0.002).