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Title: Lung tumor delineation in PET-CT images using a downhill region growing and a Gaussian mixture model
Authors: Ballangan, C
Wang, X
Fulham, M
Eberl, S
Feng, D
Keywords: keywords: {NSCLC
Tumor delineation
Tumor segmentation
Issue Date: 2011
Publisher: IEEE
Source: 2011 18th IEEE International Conference on Image Processing (ICIP), 11-14 September 2011, Brussels, p. 2173-2176 How to cite?
Abstract: Combined PET-CT is now increasingly used for the clinical evaluation of cancer and is arguably the best tool to stage non-small cell lung cancer (NSCLC). We propose a framework to better delineate lung tumors which utilizes information from PET and CT images. The framework is based on a downhill region growing technique for PET and a Gaussian mixture model for CT images. We applied our framework in 20 PET-CT studies from patients with NSCLC. Experiments show that our method is able to delineate lung tumors in complex cases where the tumors are located near other organs with similar intensities in PET images or when the tumors extends into the chest wall or the mediastinum. We also compared 10 of the datasets with experts performing manual delineation, which produced a volumetric overlapped fraction of 0.78 ± 0.10.
ISBN: 978-1-4577-1304-0
978-1-4577-1302-6 (E-ISBN)
ISSN: 1522-4880
DOI: 10.1109/ICIP.2011.6116042
Appears in Collections:Conference Paper

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