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Proceedings Paper

Validation of an enhanced knowledge-based method for segmentation and quantitative analysis of intrathoracic airway trees from three-dimensional CT images
Author(s): Milan Sonka; Wonkyu Park; Eric A. Hoffman
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Paper Abstract

Accurate assessment of airway physiology, evaluated in terms of geometric changes, is critically dependent upon the accurate imaging and image segmentation of the 3D airway tree structure. We have previously reported a knowledge-based method for 3D airway tree segmentation from high resolution CT (HRCT) images. Here we report a substantially improved version of our method. In the current implementation, the method consists of several stages. First, the lung borders are automatically determined in the 3D set of HRCT data. The primary airway tree is semi-automatically identified. In the next stage, potential airways are determined in individual CT slices using a rule- based system that uses contextual information and a priori knowledge about pulmonary anatomy. Using 3D connectivity properties of the pulmonary airway tree, the 3D tree is constructed from the set of adjacent slices. The method's performance and accuracy were assessed in five 3D HRCT canine images. Computer-identified airways matched 226/258 observer-defined airways (87.6%); the computer method failed to detect the airways in the remaining 32 locations. By visual assessment of rendered airway trees, the experienced observers judged the computer-detected airway trees as highly realistic.

Paper Details

Date Published: 24 May 1995
PDF: 9 pages
Proc. SPIE 2433, Medical Imaging 1995: Physiology and Function from Multidimensional Images, (24 May 1995); doi: 10.1117/12.209688
Show Author Affiliations
Milan Sonka, Univ. of Iowa (United States)
Wonkyu Park, Univ. of Iowa (United States)
Eric A. Hoffman, Univ. of Iowa College of Medicine (United States)

Published in SPIE Proceedings Vol. 2433:
Medical Imaging 1995: Physiology and Function from Multidimensional Images
Eric A. Hoffman, Editor(s)

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