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Title: Brain vasculature segmentation from magnetic resonance angiographic image
Keywords: Vasculature Segmentation, MRA images, Geodesic Active Contours.
Issue Date: 31-Jan-2008
Citation: DORNOOSH ZONOOBI (2008-01-31). Brain vasculature segmentation from magnetic resonance angiographic image. ScholarBank@NUS Repository.
Abstract: Precise segmentation of vasculature from three-dimensional (3D) magnetic resonance angiography (MRA) images plays an important role in image-guided neurosurgery, pre-operation planning and clinical analysis. Level sets based evolution algorithms are being widely applied to MRA data sets. Our objective is to develop an automated segmentation scheme to accurately extract vasculature of the brain, especially the thin vessels. Inspired by the intrinsic properties of MRA, we have proposed a scheme called the gradient compensated active contour (GCAC), which compensates for the low gradient near the edge of thin vesselsb contour. The GCAC, which is based on level sets, has been tested on both synthetic volumetric images and real 3D MRA images. Our experiments show that the introduced gradient compensation can facilitate more accurate segmentation of thin blood vessels.
Appears in Collections:Master's Theses (Open)

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