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Proceedings Paper

Airway tree segmentation using adaptive regions of interest
Author(s): Juerg Tschirren; Eric A. Hoffman; Geoffrey McLennan; Milan Sonka
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Paper Abstract

The accurate segmentation of the human airway tree from volumetric CT images builds an important corner stone in pulmonary image processing. It is the basis for many consecutive processing steps like branch-point labeling and matching, virtual bronchoscopy, and more. Previously reported airway tree segmentation methods often suffer from "leaking" into the surrounding lung tissue, caused by the anatomically thin airway wall combined with the occurrence of partial volume effect and noise. Another common problem with previously proposed airway segmentation algorithms is their difficulties with segmenting low dose scans and scans of heavily diseased lungs. We present a new airway tree segmentation method that works in 3D, avoids leaks, and automatically adapts to different types of scans without the need for the user to iteratively adjust any parameters.

Paper Details

Date Published: 30 April 2004
PDF: 8 pages
Proc. SPIE 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images, (30 April 2004); doi: 10.1117/12.537185
Show Author Affiliations
Juerg Tschirren, Univ. of Iowa (United States)
Eric A. Hoffman, Univ. of Iowa (United States)
Geoffrey McLennan, Univ. of Iowa (United States)
Milan Sonka, Univ. of Iowa (United States)


Published in SPIE Proceedings Vol. 5369:
Medical Imaging 2004: Physiology, Function, and Structure from Medical Images
Amir A. Amini; Armando Manduca, Editor(s)

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