Automatic detection of arterial voxels in dynamic contrast-enhanced MR images of the brain

Chan, Sze Liang Stanley and Gal, Yaniv (2012). Automatic detection of arterial voxels in dynamic contrast-enhanced MR images of the brain. In: 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA). 14th International Conference on Digital Image Computing Techniques and Applications (DICTA), 2012, Noosa Heads, QLD, Australia, (6411710.1-6411710.7). 3-5 December 2012. doi:10.1109/DICTA.2012.6411710

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Author Chan, Sze Liang Stanley
Gal, Yaniv
Title of paper Automatic detection of arterial voxels in dynamic contrast-enhanced MR images of the brain
Conference name 14th International Conference on Digital Image Computing Techniques and Applications (DICTA), 2012
Conference location Noosa Heads, QLD, Australia
Conference dates 3-5 December 2012
Proceedings title 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)
Journal name 2012 International Conference On Digital Image Computing Techniques and Applications (Dicta)
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2012
Sub-type Fully published paper
DOI 10.1109/DICTA.2012.6411710
ISBN 9781467321815
9781467321792
Start page 6411710.1
End page 6411710.7
Total pages 7
Language eng
Abstract/Summary Arterial input function (AIF) is important for the determination of cerebral blood flow and the analysis of related disease. Detection of artery voxels in dynamic contrast-enhanced (DCE) MRI is the key challenge in estimating the AIF. In the presence of tumour tissue, automatic detection of arteries becomes as even more challenging task. In this paper we propose a supervised machine-learning based method for the detection of artery voxels in DCE-MRI of the brain. The method utilises a set of kinetic and local structural features with a logistic regression classifier in order to detect arterial voxels in the image. The performance of the method is evaluated on 11 DCE-MRI datasets, of patients with diagnosed brain cancer, in terms of area under the ROC curve and in terms of correlation with an ideal AIF. The results of the evaluation suggest that the proposed method has the potential to be used as a tool for accurate estimation of AIF in DCE-MRI of the brain.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number 6411710

 
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Created: Fri, 15 Mar 2013, 02:06:43 EST by Dr Yaniv Gal on behalf of Centre for Medical Diagnostic Technologies in Qld