There has been an increased interest in automatic segmentation of volumetric medical image data. One of the reasons is that automated segmentation takes away the variability which exists when data is segmented manually. It also reduces processing time significantly. However, because of the stochastic nature of biological structures and the fact that no two data sets and scanner models are alike, it is very important to develop automated methods which process images in an adaptive manner and use a priori information to simplify the process. The method which we present here adaptively determines thresholds in order to segment out the primary human airway tree and uses some a priori information about the manner in which branching occurs, specifically the order in which the upward and downward branches arise from the right and left bronchi. We present preliminary results from this method, which automatically segments out the first four generations of the airway tree reliably, in data sets from both normal and airway comprised subjects and present comparisons with the current 'gold standard' of manual segmentation.
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