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1 May 1994 Knowledge-based segmentation of intrathoracic airways from multidimensional high-resolution CT images
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A critically important component in the development of new methods for treatment of pulmonary diseases is the development of sensitive techniques for assessing alterations in regional lung structure and function. We describe an automated method for segmentation of airway trees from 3-D sets of CT images. The method is based on a combination of conventional 3-D seeded region growing that is used to identify large airways, knowledge- based 2-D segmentation of individual CT slices to identify probable locations of smaller diameter airways, and merging of airway regions across the 3-D set of slices resulting in a tree-like airway structure. The preliminary validation of the method was done in eighty 3 mm thick CT sections from two 40 slice data sets of a canine thorax scanned with lungs held at 1.5 kPa and 2.5 kPa. The method's performance was compared with that of the conventional 3-D region growing method. The knowledge-based approach to identification of potential airways in individual image slices substantially outperforms the conventional method and promises to be applicable to in vivo pulmonary CT images.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Milan Sonka, Gopal Sundaramoorthy, and Eric A. Hoffman "Knowledge-based segmentation of intrathoracic airways from multidimensional high-resolution CT images", Proc. SPIE 2168, Medical Imaging 1994: Physiology and Function from Multidimensional Images, (1 May 1994);

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