Maximal Contrast Adaptive Region Growing for CT Airway Tree Segmentation

Description

In this paper we propose a fully self-assessed adaptive region growing airway segmentation algorithm. We rely on a standardized and self-assessed region-based approach to deal with varying imaging conditions. Initialization of the algorithm requires prior knowledge of trachea location. This can be provided either by manual seeding or by automatic trachea detection in upper airway tree image slices. The detection of the optimal parameters is managed internally using a measure of the varying contrast of the growing region. Extensive validation is provided for a set of 20 chest CT scans. Our method exhibits very low leakage into the lung parenchyma, so even though the smaller airways are not obtained from the region growing, our fully automatic technique can provide robust and accurate initialization for other methods

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