Title :
Fast Unsupervised Segmentation of 3D Magnetic Resonance Angiography
Author :
El-Baz, Ayman ; Farag, Aly ; Gimel´farb, Georgy ; El-Ghar, Mohamed Abou ; Eldiasty, T.
Author_Institution :
CVIP Lab., Louisville Univ., KY, USA
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; haemorheology; image segmentation; 3D magnetic resonance angiography; MRA images; adaptive probabilistic model; blood vessel; cerebrovascular system; laminar blood flow; turbulent blood flow; unsupervised segmentation; Angiography; Biomedical imaging; Blood flow; Blood vessels; Data mining; Deformable models; Image segmentation; Magnetic resonance; Principal component analysis; Probability; Expectation maximization; blood vessels; laminar blood flow; magnetic resonance angiography; turbulent blood flow;
Conference_Titel :
Image Processing, 2006 IEEE International Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
1-4244-0480-0
DOI :
10.1109/ICIP.2006.312370
