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Title: Extraction of metastatic lymph nodes from MR images using two deformable model-based approaches
Authors: Zhou, J.-Y. 
Fang, W.
Chan, K.-L.
Chong, V.F.H. 
Khoo, J.B.K.
Keywords: Deformable model
Level sets
Magnetic resonance imaging
Metastatic cervical nodes
Nasopharyngeal carcinoma
Object extraction
Issue Date: Dec-2007
Citation: Zhou, J.-Y., Fang, W., Chan, K.-L., Chong, V.F.H., Khoo, J.B.K. (2007-12). Extraction of metastatic lymph nodes from MR images using two deformable model-based approaches. Journal of Digital Imaging 20 (4) : 336-346. ScholarBank@NUS Repository.
Abstract: We presented and evaluated two deformable model-based approaches, region plus contour deformation (RPCD), and level sets to extract metastatic cervical nodal lesions from pretreatment T2-weighted magnetic resonance images. The RPCD method first uses a region deformation to achieve a rough boundary of the target node from a manually drawn initial contour, based on signal statistics. After that, an active contour deformation is employed to drive the rough boundary to the real node-normal tissue interface. Differently, the level sets move a manually drawn initial contour toward the desired nodal boundary under the control of the evolvement speed function, which is influenced by image gradient force. The two methods were tested by extracting 33 metastatic cervical nodes from 18 nasopharyngeal carcinoma patients. Experiments on a basis of pixel matching to reference standard showed that RPCD and level sets achieved averaged percentage matching at 82-84% and 87-88%, respectively. In addition, both methods had significantly lower interoperator variances than the manual tracing method. It was suggested these two methods could be useful tools for the evaluation of metastatic nodal volume as an indicator of classification and treatment response, or be alternatives for the delineation of metastatic nodal lesions in radiation treatment planning. © 2007 SCAR (Society for Computer Applications in Radiology).
Source Title: Journal of Digital Imaging
ISSN: 08971889
DOI: 10.1007/s10278-006-1037-2
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