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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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