Nonlinear image registration with bidirectional metric and reciprocal regularization.
Nonlinear image registration with bidirectional metric and reciprocal regularization.
复制标题
具有双向度量和倒数正则化的非线性图像配准
DOI：
10.1371/journal.pone.0172432
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Xu M
中科院分区：
文献类型：
--
作者：
Ying S;Li D;Xiao B;Peng Y;Du S;Xu M
非线性配准是一种重要的图像配准技术，在医学图像分析中有着广泛的应用。本文提出了一种新的基于微分同胚的非线性配准框架，其中引入了一个倒数正则化子来假设两幅图像之间的变形是精确的微分同胚。具体来说，首先，我们采用双向度规来改善能量泛函的对称性，其变量是两个倒数形变。其次，我们将这两个形变松弛为两个独立变量，并引入一个倒数正则化子来确保这两个形变是精确的微分同态。然后，我们利用交替迭代策略将模型解耦为两个极小子问题，在那里计算了近似变形速度的新的闭合形式。最后，我们在两个真实的脑MR图像数据集上与两种相对的和常规的方法进行了比较。实验结果表明，该方法提高了配准的精确度和稳健性，并且得到的双向变形实际上是互反的。
Nonlinear registration is an important technique to align two different images and widely applied in medical image analysis. In this paper, we develop a novel nonlinear registration framework based on the diffeomorphic demons, where a reciprocal regularizer is introduced to assume that the deformation between two images is an exact diffeomorphism. In detail, first, we adopt a bidirectional metric to improve the symmetry of the energy functional, whose variables are two reciprocal deformations. Secondly, we slack these two deformations into two independent variables and introduce a reciprocal regularizer to assure the deformations being the exact diffeomorphism. Then, we utilize an alternating iterative strategy to decouple the model into two minimizing subproblems, where a new closed form for the approximate velocity of deformation is calculated. Finally, we compare our proposed algorithm on two data sets of real brain MR images with two relative and conventional methods. The results validate that our proposed method improves accuracy and robustness of registration, as well as the gained bidirectional deformations are actually reciprocal.