title: Magnetic resonance angiography: from anatomical knowledge modeling to vessel segmentation.
abstract: Magnetic resonance angiography (MRA) has become a common way to study cerebral vascular structures. Indeed, it enables to obtain information on flowing blood in a totally non-invasive and non-irradiant fashion. MRA exams are generally performed for three main applications: detection of vascular pathologies, neurosurgery planning, and vascular landmark detection for brain functional analysis. This large field of applications justifies the necessity to provide efficient vessel segmentation tools. Several methods have been proposed during the last fifteen years. However, the obtained results are still not fully satisfying. A solution to improve brain vessel segmentation from MRA data could consist in integrating high-level a priori knowledge in the segmentation process. A preliminary attempt to integrate such knowledge is proposed here. It is composed of two methods devoted to phase contrast MRA (PC MRA) data. The first method is a cerebral vascular atlas creation process, composed of three steps: knowledge extraction, registration, and data fusion. Knowledge extraction is performed using a vessel size determination algorithm based on skeletonization, while a topology preserving non-rigid registration method is used to fuse the information into the atlas. The second method is a segmentation process involving adaptive sets of gray-level hit-or-miss operators. It uses anatomical knowledge modeled by the cerebral vascular atlas to adapt the parameters of these operators (number, size, and orientation) to the searched vascular structures. These two methods have been tested by creating an atlas from a 18 MRA database, and by using it to segment 30 MRA images, comparing the results to those obtained from a region-growing segmentation method.
authors.affiliation: Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection (LSIIT), UMR 7005 CNRS-ULP, Bd S. Brant, BP 10413, F-67412 Illkirch Cedex, . passat@dpt-info.u-strasbg.fr
authors.forename: N
authors.initials: N
authors.lastname: Passat
authors.forename: C
authors.initials: C
authors.lastname: Ronse
authors.forename: J
authors.initials: J
authors.lastname: Baruthio
authors.forename: J-P
authors.initials: JP
authors.lastname: Armspach
authors.forename: C
authors.initials: C
authors.lastname: Maillot
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002536:Cerebral Arteries / Q000033:anatomy & histology*; D003198:Computer Simulation; D016208:Databases, Factual; 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*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique