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Knowledge of airway tree morphology has important clinical applications indiagnosis of chronic obstructive pulmonary disease. We present an automatictree extraction method based on multiple hypothesis tracking and templatematching for this purpose and evaluate its performance on chest CT images. Themethod is adapted from a semi-automatic method devised for vessel segmentation.Idealized tubular templates are constructed that match airway probabilityobtained from a trained classifier and ranked based on their relativesignificance. Several such regularly spaced templates form the local hypothesesused in constructing a multiple hypothesis tree, which is then traversed toreach decisions. The proposed modifications remove the need for localthresholding of hypotheses as decisions are made entirely based on statisticalcomparisons involving the hypothesis tree. The results show improvements inperformance when compared to the original method and region growing onintensity images. We also compare the method with region growing on theprobability images, where the presented method does not show substantialimprovement, but we expect it to be less sensitive to local anomalies in thedata.

Extraction of airway trees using multiple hypothesis tracking and template matching