title: An improved cerebral vessel extraction method for MRA images.
abstract: To improve the speed and accuracy of cerebral vessel extraction, a fast and robust method is proposed in this paper. First, volume data are divided into sub-volumes by using octree, and at the same time invalid volume data are eliminated. Second, fuzzy connectedness is introduced to achieve fast cerebral vessel segmentation from 3D MRA Images. The values of gradient and Laplacian transformation are then calculated to improve the accuracy of the distance field. Last, the center of gravity is utilized to refine the initial centerline to make it closer to the actual centerline of the vessel cavity. The experiment demonstrates that the proposed method can effectively improve the speed and precision of centerline extraction.
authors.affiliation: School of Computer, Wuhan University, Wuhan, China.
authors.forename: Hua
authors.initials: H
authors.lastname: Zou
authors.affiliation: School of Computer, Wuhan University, Wuhan, China.
authors.forename: Wen
authors.initials: W
authors.lastname: Zhang
authors.affiliation: School of Computer, Wuhan University, Wuhan, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
mesh_terms: D002536:Cerebral Arteries / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D002532:Intracranial Aneurysm / Q000473:pathology*; D000069550:Machine Learning; D018810:Magnetic Resonance Angiography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Segmentation; centerline extraction; cerebrovascular; fuzzy connectedness; image segmentation