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Proceedings Paper

Segmentation and quantitation of the primary human airway tree
Author(s): Rajendra Chiplunkar; Joseph M. Reinhardt; Eric A. Hoffman
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Paper Abstract

There has been an increased interest in automatic segmentation of volumetric medical image data. One of the reasons is that automated segmentation takes away the variability which exists when data is segmented manually. It also reduces processing time significantly. However, because of the stochastic nature of biological structures and the fact that no two data sets and scanner models are alike, it is very important to develop automated methods which process images in an adaptive manner and use a priori information to simplify the process. The method which we present here adaptively determines thresholds in order to segment out the primary human airway tree and uses some a priori information about the manner in which branching occurs, specifically the order in which the upward and downward branches arise from the right and left bronchi. We present preliminary results from this method, which automatically segments out the first four generations of the airway tree reliably, in data sets from both normal and airway comprised subjects and present comparisons with the current 'gold standard' of manual segmentation.

Paper Details

Date Published: 9 May 1997
PDF: 12 pages
Proc. SPIE 3033, Medical Imaging 1997: Physiology and Function from Multidimensional Images, (9 May 1997); doi: 10.1117/12.274066
Show Author Affiliations
Rajendra Chiplunkar, Univ. of Iowa College of Medicine (United States)
Joseph M. Reinhardt, Univ. of Iowa College of Medicine (United States)
Eric A. Hoffman, Univ. of Iowa College of Medicine (United States)


Published in SPIE Proceedings Vol. 3033:
Medical Imaging 1997: Physiology and Function from Multidimensional Images
Eric A. Hoffman, Editor(s)

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