title: Multimodality cardiovascular image segmentation using a deformable contour model.
abstract: An automatic segmentation method has been developed for cardiovascular multimodality imaging. A "snake" model based on a curve shaping and an energy-minimizing process is used to detect blood-wall interfaces on Cine-CT, MRI and ultrasound images. Deformation of a reduced set of contour points was made according to a discretized global, regional and local minimum energy criterion. A continuous regional optimization process was also integrated into the deformation model, it takes into account a cubic spline interpolation and adaptive regularity constraints. The constraints provided rapid convergence toward a final contour position by successively stopping spline segments.
authors.affiliation: INSERM U-66, CHU Pitié, Paris, France.
authors.forename: A
authors.initials: A
authors.lastname: Sebbahi
authors.forename: A
authors.initials: A
authors.lastname: Herment
authors.forename: A
authors.initials: A
authors.lastname: de Cesare
authors.lastname: Mousseaux
mesh_terms: D000465:Algorithms; D001769:Blood; D001808:Blood Vessels / Q000033:anatomy & histology; D002319:Cardiovascular System / Q000033:anatomy & histology*; D002933:Cineradiography; D003952:Diagnostic Imaging; D004452:Echocardiography; D006321:Heart / Q000033:anatomy & histology; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D019028:Magnetic Resonance Imaging, Cine; D008955:Models, Cardiovascular; D011856:Radiographic Image Enhancement; D014057:Tomography, X-Ray Computed; D018084:Ultrasonography, Interventional