A flexible 3D cerebrovascular extraction from TOF-MRA images

For accurately extracting 3D cerebral tree from time-of-flight magnetic resonance angiography (TOF-MRA) images, a novel active contour model is presented by combining the statistical information and the vessel shape information in a variational level set framework. Firstly, a finite mixture model of four Gaussian distributions is proposed to model the statistical distribution of the density of TOF-MRA data, which can excellently describe the distribution of the cerebral vessels and background. Secondly, a vascular vector field, derived from the eigenanalysis of the Hessian matrix, is employed to model vessel shape information, which can guide the contour evolving along the vessel center line towards the thin or weak vessels. Thirdly, an automatic method of initializing the contour, derived from enhancement of the vessel shape, is proposed, which is capable of setting the initial contour efficiently and automatically. Furthermore, the narrow band technique is adopted, which effectively reduces the computation. Finally, a speedup strategy in the implementation is developed by labeling the steadily-evolved points, which avoids the repeated computation of these points in the later iterations. Experiments are made with several classical models over 9 MRA datasets, and the results show that the proposed model outperforms the classical models.