Yushkevich Paul A
Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, 3600 Market St., Ste 370, Philadelphia, PA 19104, USA.
Neuroimage. 2009 Mar;45(1 Suppl):S99-110. doi: 10.1016/j.neuroimage.2008.10.051. Epub 2008 Nov 12.
A new approach for constructing deformable continuous medial models for anatomical structures is presented. Medial models describe geometrical objects by first specifying the skeleton of the object and then deriving the boundary surface corresponding to the skeleton. However, an arbitrary specification of a skeleton will not be "valid" unless a certain set of sufficient conditions is satisfied. The most challenging of these is the non-linear equality constraint that must hold along the boundaries of the manifolds forming the skeleton. The main contribution of this paper is to leverage the biharmonic partial differential equation as a mapping from a codimension-0 subset of Euclidean space to the space of skeletons that satisfy the equality constraint. The PDE supports robust numerical solution on freeform triangular meshes, providing additional flexibility for shape modeling. The approach is evaluated by generating continuous medial models for a large dataset of hippocampus shapes. Generalizations to modeling more complex shapes and to representing branching skeletons are demonstrated.
提出了一种构建解剖结构可变形连续中间模型的新方法。中间模型通过首先指定物体的骨架,然后推导与骨架对应的边界表面来描述几何物体。然而,除非满足一组特定的充分条件,否则骨架的任意指定将是“无效的”。其中最具挑战性的是必须沿构成骨架的流形边界成立的非线性等式约束。本文的主要贡献是利用双调和偏微分方程作为从欧几里得空间的余维数为0的子集到满足等式约束的骨架空间的映射。该偏微分方程支持在自由形式三角形网格上进行鲁棒的数值求解,为形状建模提供了额外的灵活性。通过为大量海马形状数据集生成连续中间模型来评估该方法。展示了对更复杂形状建模和表示分支骨架的推广。