Abstract: Accurate assessment of intrathoracic airway physiology requires sophisticated imaging and image segmentation of the three-dimensional airway tree structure. We have previously reported a rule-based method for three-dimensional airway tree segmentation from electron beam CT (EBCT) images. Here we report a new approach to airway tree segmentation in which fuzzy logic is used for image interpretation. In canine EBCT images, airways identified by the fuzzy logic method matched 276/337 observer-defined airways (81.9%) while the fuzzy method failed to detect the airways in the remaining 61 observer-determined locations (18.1%). By comparing the performance of the new fuzzy logic method and that of our former rule-based method, the fuzzy logic method significantly decreased the number of false airways (p less than 0.001). !13
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