title: A new adaptive probabilistic model of blood vessels for segmenting MRA images.
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.
authors.affiliation: Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY 40292, USA. elbaz@cvip.Louisville.edu
authors.forename: Ayman
authors.initials: A
authors.lastname: El-Baz
authors.forename: Aly A
authors.initials: AA
authors.lastname: Farag
authors.forename: Georgy
authors.initials: G
authors.lastname: Gimel'farb
authors.forename: Mohamed A
authors.initials: MA
authors.lastname: El-Ghar
authors.forename: Tarek
authors.initials: T
authors.lastname: Eldiasty
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001808:Blood Vessels / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008955:Models, Cardiovascular; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity