Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The fast construction of extension velocities in level set methods
Journal of Computational Physics
Co-dimension 2 Geodesic Active Contours for MRA Segmentation
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
Medical image segmentation using topologically adaptable surfaces
CVRMed-MRCAS '97 Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery
Statistical 3D Vessel Segmentation Using a Rician Distribution
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Fusing Speed and Phase Information for Vascular Segmentation in Phase Contrast MR Angiograms
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
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This paper presents a new and integrated approach to automatic 3D brain vessel segmentation using physics-based statistical models of background and vascular signals, and velocity (flow) field information in phase contrast magnetic resonance angiograms (PC-MRA). The proposed new approach makes use of realistic statistical models to detect vessels more accurately than conventional intensity gradient-based approaches. In this paper, rather than using MRA speed images alone, as in prior work [7,8,10], we define a 3D local phase coherence (LPC) measure to incorporate velocity field information. The proposed new approach is an extension of our previous work in 2D vascular segmentation [5,6], and is formulated in a variational framework, which is implemented using the recently proposed modified level set method [1]. Experiments on flow phantoms, as well as on clinical data sets, show that our approach can segment normal vasculature as well as low flow (low SNR) or complex flow regions, especially in an aneurysm.