title: Joint model-pixel segmentation with pose-invariant deformable graph-priors.
abstract: This paper proposes a novel framework for image segmentation through a unified model-based and pixel-driven integrated graphical model. Prior knowledge is expressed through the deformation of a discrete model that consists of decomposing the shape of interest into a set of higher order cliques (triplets). Such decomposition allows the introduction of region-driven image statistics as well as pose-invariant (i.e. translation, rotation and scale) constraints whose accumulation introduces global deformation constraints on the model. Regional triangles are associated with pixels labeling which aims to create consistency between the model and the image space. The proposed formulation is pose-invariant, can integrate regional statistics in a natural and efficient manner while being able to produce solutions unobserved during training. The challenging problem of tagged cardiac MR image segmentation is used to demonstrate the performance potentials of the method.
authors: Center for Visual Computing, Ecole Centrale de Paris, France.
authors: Bo
authors: B
authors: Xiang
authors: Radiology Department, Henri Mondor Hospital, Créteil, France.
authors: Jean-Francois
authors: JF
authors: Deux
authors: Radiology Department, Henri Mondor Hospital, Créteil, France.
authors: Alain
authors: A
authors: Rahmouni
authors: Center for Visual Computing, Ecole Centrale de Paris, France.
authors: Nikos
authors: N
authors: Paragios
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006321:Heart / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008955:Models, Cardiovascular; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique