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Adaptive FEM-based nonrigid image registration using truncated hierarchical B-splines

机译:使用截断的分层B样条的基于自适应FEM的非刚性图像配准

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We present an efficient approach of Finite Element Method (FEM)-based nonrigid image registration, in which the spatial transformation is constructed using truncated hierarchical B-splines (THB-splines). The image registration framework minimizes an energy functional using an FEM-based method and thus involves solving a large system of linear equations. This framework is carried out on a set of successively refined grids. However, due to the increased number of control points during subdivision, large linear systems are generated which are generally demanding to solve. Instead of using uniform subdivision, an adaptive local refinement scheme is carried out, only refining the areas of large change in deformation of the image. By incorporating the key advantages of THB-spline basis functions such as linear independence, partition of unity and reduced overlap into the FEM-based framework, we improve the matrix sparsity and computational efficiency. The performance of the proposed method is demonstrated on 2D synthetic and medical images. (C) 2016 Elsevier Ltd. All rights reserved.
机译:我们提出一种基于有限元方法(FEM)的非刚性图像配准的有效方法,其中使用截短的分层B样条(THB样条)构建空间变换。图像配准框架使用基于FEM的方法最小化能量函数,因此涉及求解大型线性方程组。该框架是在一组连续完善的网格上执行的。但是,由于细分期间控制点数量的增加,生成了通常需要解决的大型线性系统。代替使用均匀细分,而是执行自适应局部细化方案,仅细化图像变形中较大变化的区域。通过将THB样条基函数的主要优点(如线性独立性,单位分配和减少的重叠)纳入基于FEM的框架中,我们提高了矩阵稀疏性和计算效率。在二维合成图像和医学图像上证明了该方法的性能。 (C)2016 Elsevier Ltd.保留所有权利。

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