Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast level set method for propagating interfaces
Journal of Computational Physics
International Journal of Computer Vision
PDE and Level Sets: Algorithmic Approaches to Static and Motion Imagery
PDE and Level Sets: Algorithmic Approaches to Static and Motion Imagery
Hyperbolic "Smoothing" of Shapes
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
MRA image segmentation with capillary active contour
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II
IEEE Transactions on Information Technology in Biomedicine
Area and length minimizing flows for shape segmentation
IEEE Transactions on Image Processing
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Precise segmentation of vasculature from three-dimensional (3D) magnetic resonance angiography (MRA) images is playing an important role in image-guided neurosurgery, pre-operation planning and clinical analysis. Active Contour based evolution algorithms are being widely applied to MRA data sets, however existing approaches exhibit some difficulties in extracting tiny parts of the vessels. Our objective is to develop an automated segmentation scheme to accurately extract vasculature of the brain, especially tiny vessels. Inspired by the intrinsic properties of MRA, we have proposed a scheme called the gradient compensated geodesic active contours (GCGAC), which compensates for low gradients near edges of thin vessels. The GCGAC, which is implemented based on level set, has been tested on both synthetic volumetric image and real 3D MRA images. Our experiments show that the introduced gradient compensation can facilitate more accurate segmentation of tiny blood vessels.