DocumentCode :
2823533
Title :
Lung tumor delineation in PET-CT images using a downhill region growing and a Gaussian mixture model
Author :
Ballangan, Cherry ; Wang, Xiuying ; Fulham, Michael ; Eberl, Stefan ; Feng, Dagan
Author_Institution :
Biomed. & Multimedia Inf. Technol. (BMIT) Res. Group, Sydney Univ., Sydney, NSW, Australia
fYear :
2011
fDate :
11-14 Sept. 2011
Firstpage :
2173
Lastpage :
2176
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.
Keywords :
Gaussian processes; cancer; lung; medical image processing; positron emission tomography; tumours; Gaussian mixture model; PET-CT images; cancer; chest wall; downhill region growing technique; lung tumor delineation; mediastinum; nonsmall cell lung cancer; Biomedical imaging; Computed tomography; Image segmentation; Liver; Lungs; Positron emission tomography; Tumors; NSCLC; PET-CT; Tumor delineation; tumor segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2011 18th IEEE International Conference on
Conference_Location :
Brussels
ISSN :
1522-4880
Print_ISBN :
978-1-4577-1304-0
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2011.6116042
Filename :
6116042
Link To Document :
بازگشت