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Title: Graph-based Mumford-Shah segmentation of dynamic PET with application to input function estimation
Authors: Parker, BJ
Feng, DD
Keywords: Brain modeling
Covariance analysis
Graph theory
Image segmentation
Pattern recognition
Positron emission tomography
Issue Date: Feb-2005
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on nuclear science, Feb. 2005, v. 52, no. 1, p. 79-89 How to cite?
Journal: IEEE transactions on nuclear science 
Abstract: A graph-theoretic three-dimensional (3-D) segmentation algorithm based on Mumford-Shah energy minimization is applied to the segmentation of brain [sup 18]F-fluoro-deoxyglucose (FDG) dynamic positron emission tomography data for the automated extraction of tissues with distinct time activity curves (TACs), and, in particular, extraction of the internal carotid arteries and venous sinuses for the noninvasive estimation of the input arterial TAC. Preprocessing by principal component analysis (PCA) and a Mahalanobis distance metric provide segmentation based on distinct TAC shape rather than simply activity levels. Evaluations on simulation and clinical FDG brain positron emission tomography (PET) studies demonstrate that differing tissue types can be accurately demarcated with superior performance to k-means clustering approaches, and, in particular, the internal carotids and venous sinuses can be robustly segmented in clinical brain dynamic PET datasets, allowing for the fully automatic noninvasive estimation of the arterial input curve.
ISSN: 0018-9499
EISSN: 1558-1578
DOI: 10.1109/TNS.2004.843133
Rights: © 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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