Abstract
A statistical shape model that accurately generalizes a family of 3D shapes requires establishing correspondences across the set of shapes. However in 3D anatomical meshes, finding a sufficient number of landmarks to accurately describe the shape can be a challenge, and often only a few points are easily identifiable due to the smooth nature of the object surface. Using a sparse set of landmarks, this paper finds a dense set of vertex correspondences across a set of 3D aortic root meshes. This is achieved by non-rigidly transforming a source mesh to a target mesh. Then, for every vertex on the target, a corresponding vertex on the deformed source is found, resulting in complete correspondence. A more accurate transformation results in better correspondence establishment, and our mesh registration experiments show an average Hausdorff distance of 3.65mm, and an average point-to-mesh distance of 0.41mm, i. e. within one voxel.
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