Validation of an enhanced knowledge-based method for segmentation and quantitative analysis of intrathoracic airway trees from three-dimensional CT images
- Univ. of Iowa, Iowa City, IA (United States)
Accurate assessment of airway physiology, evaluated in terms of geometric changes, is critically dependent upon the accurate imaging and image segmentation of the three-dimensional airway tree structure. The authors have previously reported a knowledge-based method for three-dimensional airway tree segmentation from high resolution CT (HRCT) images. Here, they report a substantially improved version of the method. In the current implementation, the method consists of several stages. First, the lung borders are automatically determined in the three-dimensional 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 three-dimensional connectivity properties of the pulmonary airway tree, the three-dimensional 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.
- OSTI ID:
- 372492
- Report Number(s):
- CONF-9502135-; ISBN 0-8194-1781-5; TRN: 96:024170
- Resource Relation:
- Conference: SPIE medical imaging 1995 conference, San Diego, CA (United States), 27-28 Feb 1995; Other Information: PBD: 1995; Related Information: Is Part Of Medical imaging 1995: Physiology and function from multidimensional images; Hoffman, E.A. [ed.]; PB: 443 p.; Proceedings/SPIE, Volume 2433
- Country of Publication:
- United States
- Language:
- English
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