Conference proceeding
Airway tree segmentation using adaptive regions of interest
Proceedings of SPIE, Vol.5369(1), pp.117-124
Medical Imaging 2004: Physiology, Function, and Structure from Medical Images
04/30/2004
DOI: 10.1117/12.537185
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.
Details
- Title: Subtitle
- Airway tree segmentation using adaptive regions of interest
- Creators
- Juerg Tschirren - Univ. of Iowa (USA)Eric A Hoffman - Univ. of Iowa (USA)Geoffrey McLennan - Univ. of Iowa (USA)Milan Sonka - Univ. of Iowa (USA)
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of SPIE, Vol.5369(1), pp.117-124
- Conference
- Medical Imaging 2004: Physiology, Function, and Structure from Medical Images
- DOI
- 10.1117/12.537185
- ISSN
- 0277-786X
- Language
- English
- Date published
- 04/30/2004
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Radiation Oncology; Injury Prevention Research Center; Internal Medicine; Ophthalmology and Visual Sciences
- Record Identifier
- 9984047683302771
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