Abstract #3759
Venous segmentation using Gaussian mixture models and Markov random fields
Phillip G. D. Ward 1,2 , Nicholas J. Ferris 2,3 , Amanda C. L. Ng 2,4 , David G. Barnes 1,5 , David L. Dowe 1 , Gary F. Egan 2,6 , and Parnesh Raniga 2
1
Clayton School of Information Technology,
Monash University, Clayton, Victoria, Australia,
2
Monash
Biomedical Imaging, Monash University, Clayton,
Victoria, Australia,
3
Monash
Imaging, Monash Health, Clayton, Victoria, Australia,
4
Department
of Anatomy and Neuroscience, The University of
Melbourne, Parkville, Victoria, Australia,
5
Monash
eResearch Centre, Monash University, Victoria,
Australia,
6
School
of Psychology and Psychiatry, Monash University,
Victoria, Australia
This study introduces a new method for segmenting the
cerebral venous vasculature, using quantitative
susceptibility mapping (QSM) and susceptibility-weighted
imaging (SWI). The method employs a Gaussian
mixture-model to incorporate the QSM and SWI contrast,
which then feds into a Markov random field model,
augmented with a Gabor filter bank, to enhance
hyper-intense, vessel-like structures and provide
patient-specific venous cerebrovascular models.
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