Conference Proceeding

Graph-based energy-minimization segmentation and PCA applied to internal carotid extraction in neurological PET

Dept. of Comput. Sci., Sydney Univ., NSW, Australia
11/2003; DOI:10.1109/NSSMIC.2003.1352425 ISBN: 0-7803-8257-9 In proceeding of: Nuclear Science Symposium Conference Record, 2003 IEEE, Volume: 4
Source: IEEE Xplore

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.

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    Article: Graph-based Mumford-Shah segmentation of dynamic PET with application to input function estimation
    B.J. Parker, Dagan Feng
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    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</sup>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.
    IEEE Transactions on Nuclear Science 03/2005; · 1.45 Impact Factor
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

Keywords

automatic estimation
 
brain FDG dynamic positron emission tomography data
 
clinical FDG brain PET studies
 
graph-theoretic segmentation algorithm
 
input plasma time activity curve
 
internal carotid arteries
 
Mumford-Shah energy minimization
 
noninvasive estimation
 
principal component analysis
 
regions
 
typical dynamic PET data sets