Active-contour-based image segmentation using machine learning techniques.
We introduce a non-linear shape prior for the deformable model framework that we learn from a set of shape samples using recent manifold learning techniques. We model a category of shapes as a finite dimensional manifold which we approximate using Diffusion maps. Our method computes a Delaunay triangulation of the reduced space, considered as Euclidean, and uses the resulting space partition to identify the closest neighbors of any given shape based on its Nyström extension. We derive a non-linear shape prior term designed to attract a shape towards the shape prior manifold at given constant embedding. Results on shapes of ventricle nuclei demonstrate the potential of our method for segmentation tasks.
Odyssée Team / Certis - Ecole des Ponts, France. etyngier@certis.enpc.fr
Patrick
P
Etyngier
Florent
F
Ségonne
Renaud
R
Keriven
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D002552:Cerebral Ventricles / Q000473:pathology*; D003704:Dementia / Q000175:diagnosis*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity