title: Effective statistical edge integration using a flux maximizing scheme for volumetric vascular segmentation in MRA.
abstract: Evolutionary schemes based on the level set theory are effective tools for medical image segmentation. In this paper, a new variational technique for edge integration is presented. Region statistical measures and orientation information from ramp-like edges, are fused within an energy minimization scheme that is based on a new interpretation of edge concept. A region driven advection term simulating the edge strength effect is directly obtained from this minimization strategy. We have applied our method to several real Magnetic Resonance Angiography data sets and comparison has been made with a state-of-the-art vessel segmentation method. Presented results indicate that using this method a significant improvement is achievable and the method can be an effective tool to extract vessels in MRA intracranial images.
authors.affiliation: Graduate School of Information Science and Technology, the University of Tokyo, 7-3-1, Hongo, Bunkyo, Tokyo. gooya@atre.t.u-tokyo.ac.jp
authors.forename: Ali
authors.initials: A
authors.lastname: Gooya
authors.forename: Hongen
authors.initials: H
authors.lastname: Liao
authors.forename: Kiyoshi
authors.initials: K
authors.lastname: Matsumiya
authors.forename: Ken
authors.initials: K
authors.lastname: Masamune
authors.forename: Takeyoshi
authors.initials: T
authors.lastname: Dohi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008955:Models, Cardiovascular; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity