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Invariant surface alignment in the presence of affine and some nonlinear transformations

机译:仿射存在下的不变曲面对齐和一些非线性变换

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Introduces a non-iterative geometric-based method to align 3D brain surfaces into standard coordinate system (SCS), which is based on a novel set of surface landmarks (e.g., inflection and/or zero torsion points residing on the parabolic contours), which are intrinsic and are computed from the differential geometry of the surface. This is in contrast to existing methods that depend on anatomical landmarks that require expert intervention to locate-a very hard task. The landmarks are local and are preserved under affine transformations. To reduce the sensitivity of the landmarks to noise, the authors use a B-Spline surface representation that smooths out the surface prior to the computation of the landmarks. The alignment is achieved by establishing correspondences between the landmarks after a conformal sorting based on derived absolute invariants (volumes confined between parallel-pipeds spanned by sets of the landmark point quadruplets). The method is tested for intra- and inter-brain alignments while entertaining cubic nonlinear transformations.
机译:介绍了一种非迭代的几何基于几何方法,将3D脑表面对准标准坐标系(SCS),该系统基于一组新颖的表面地标(例如,栖息在抛物面轮廓上的拐点和/或零扭转点)是内在的,并从表面的差分几何形状计算。这与现有方法形成鲜明对比,这取决于需要专家干预的解剖标识来定位一项非常艰难的任务。地标是本地的,保存在仿射变换下。为了降低地标对噪声的敏感性,作者使用B样条表面表示,在计算地标之前平滑了表面。通过在基于派生的绝对不变量(由地标点四分比度跨越的并行管道之间限制在并行管道之间)之间的共形分类之后建立地标之间的对应来实现对准。在娱乐立方非线性变换的同时测试该方法,同时进行脑内脑内对齐。

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