30 August 2005 Accurate 3D quantification of the bronchial parameters in MDCT
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Proceedings Volume 5916, Mathematical Methods in Pattern and Image Analysis; 59160X (2005); doi: 10.1117/12.617669
Event: Optics and Photonics 2005, 2005, San Diego, California, United States
Abstract
The assessment of bronchial reactivity and wall remodeling in asthma plays a crucial role in better understanding such a disease and evaluating therapeutic responses. Today, multi-detector computed tomography (MDCT) makes it possible to perform an accurate estimation of bronchial parameters (lumen and wall areas) by allowing a quantitative analysis in a cross-section plane orthogonal to the bronchus axis. This paper provides the tools for such an analysis by developing a 3D investigation method which relies on 3D reconstruction of bronchial lumen and central axis computation. Cross-section images at bronchial locations interactively selected along the central axis are generated at appropriate spatial resolution. An automated approach is then developed for accurately segmenting the inner and outer bronchi contours on the cross-section images. It combines mathematical morphology operators, such as "connection cost", and energy-controlled propagation in order to overcome the difficulties raised by vessel adjacencies and wall irregularities. The segmentation accuracy was validated with respect to a 3D mathematically-modeled phantom of a pair bronchus-vessel which mimics the characteristics of real data in terms of gray-level distribution, caliber and orientation. When applying the developed quantification approach to such a model with calibers ranging from 3 to 10 mm diameter, the lumen area relative errors varied from 3.7% to 0.15%, while the bronchus area was estimated with a relative error less than 5.1%.
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A. Saragaglia, C. Fetita, F. Preteux, P. Y. Brillet, P. A. Grenier, "Accurate 3D quantification of the bronchial parameters in MDCT", Proc. SPIE 5916, Mathematical Methods in Pattern and Image Analysis, 59160X (30 August 2005); doi: 10.1117/12.617669; https://doi.org/10.1117/12.617669
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KEYWORDS
Image segmentation

3D modeling

Error analysis

Computed tomography

Lung

Mathematical morphology

3D image processing

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