Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15810
Title: Graph-based energy-minimization segmentation and PCA applied to internal carotid extraction in neurological PET
Authors: Parker, B
Feng, D
Keywords: Biological organs
Brain
Graph theory
Image segmentation
Medical image processing
Neurophysiology
Positron emission tomography
Principal component analysis
Issue Date: 2003
Publisher: IEEE
Source: 2003 IEEE Nuclear Science Symposium Conference Record, 19-25 October 2003, v. 4, p. 2613-2617 How to cite?
Journal: 2003 IEEE Nuclear Science Symposium Conference Record, 19-25 October 2003 
Abstract: An unseeded, graph-theoretic segmentation algorithm based on Mumford-Shah energy minimization is applied to segmentation of brain FDG dynamic positron emission tomography data after preprocessing by principal component analysis, for the automated extraction of regions of interest, and, in particular, extraction of the internal carotid arteries and venous sinuses for the noninvasive estimation of the input plasma time activity curve. Evaluation on clinical FDG brain PET studies show that the internal carotids and venous sinuses can be robustly segmented in typical dynamic PET data sets, allowing for the fully automatic estimation of the arterial input curve.
URI: http://hdl.handle.net/10397/15810
ISBN: 0-7803-8257-9
ISSN: 1082-3654
DOI: 10.1109/NSSMIC.2003.1352425
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