A new adaptive probabilistic model of blood vessels for segmenting MRA images

Authors:
Ayman El-Baz;Aly A. Farag;Georgy Gimel’farb;Mohamed A. El-Ghar;Tarek Eldiasty
Affiliations:
Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY;Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY;Department of Computer Science, University of Auckland, Auckland, New Zealand;Urology and Nephrology Department, University of Mansoura, Mansoura, Egypt;Urology and Nephrology Department, University of Mansoura, Mansoura, Egypt
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2006

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Abstract

A new physically justified adaptive probabilistic model of blood vessels on magnetic resonance angiography (MRA) images is proposed. The model accounts for both laminar (for normal subjects) and turbulent blood flow (in abnormal cases like anemia or stenosis) and results in a fast algorithm for extracting a 3D cerebrovascular system from the MRA data. Experiments with synthetic and 50 real data sets confirm the high accuracy of the proposed approach.