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Spatially Constrained Random Walk Approach for Accurate Estimation of Airway Wall Surfaces
Ziyue Xu, Ulas Bagci, Brent Foster, Awais Mansoor, and Daniel J. Mollura
Center for Infectious Disease Imaging (CIDI), Department of Radiology and Imaging Sciences, National Institutes of Health (NIH), Bethesda, MD 20892, USAziyue.xu@nih.gov
Abstract. Assessing airway wall surfaces and the lumen from high resolution computed tomography (CT) scans are of great importance for diagnosing pulmonary diseases. However, accurately determining inner and outer airway wall surfaces of a complete 3-D tree structure can be quite challenging because of its complex nature. In this paper, we introduce a computational framework to accurately quantify airways through (i) a precise segmentation of the lumen, and (ii) a spatially constrained Markov random walk method to estimate the airway walls. Our results demonstrate that the proposed airway analysis platform identified the inner and outer airway surfaces better than methods commonly used in clinics, such as full width at half maximum and phase congruency.
Keywords: lumen segmentation, airway wall estimation, fuzzy connectivity, random walk, full width at half maximum, phase congruency
LNCS 8150, p. 559 ff.
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© Springer-Verlag Berlin Heidelberg 2013