Title :
Probabilistic Modeling of Blood Vessels for Segmenting MRA Images
Author :
El-Baz, Ayman ; Farag, Aly ; Gimel´farb, Georgy ; El-Ghar, Mohamed A. ; Eldiasty, Tarek
Author_Institution :
CVIP Lab., Louisville Univ., KY
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 real data sets confirm the high accuracy of the proposed approach
Keywords :
biomedical MRI; blood vessels; haemodynamics; image segmentation; laminar flow; probability; turbulence; MRA image segmentation; adaptive probabilistic model; blood vessels; cerebrovascular system; magnetic resonance angiography images; probabilistic modeling; turbulent blood flow; Angiography; Biomedical imaging; Blood flow; Blood vessels; Data mining; Image segmentation; Magnetization; Principal component analysis; Rough surfaces; Surface roughness;
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2521-0
DOI :
10.1109/ICPR.2006.946
