He Jianchun, Christensen Gary E
Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA.
Inf Process Med Imaging. 2003 Jul;18:438-49. doi: 10.1007/978-3-540-45087-0_37.
This paper presents a new image registration algorithm that accommodates locally large nonlinear deformations. The algorithm concurrently estimates the forward and reverse transformations between a pair of images while minimizing the inverse consistency error between the transformations. It assumes that the two images to be registered contain topologically similar objects and were collected using the same imaging modality. The large deformation transformation from one image to the other is accommodated by concatenating a sequence of small deformation transformations. Each incremental transformation is regularized using a linear elastic continuum mechanical model. Results of ten 2D and twelve 3D MR image registration experiments are presented that tested the algorithm's performance on real brain shapes. For these experiments, the inverse consistency error was reduced on average by 50 times in 2D and 30 times in 3D compared to the viscous fluid registration algorithm.
本文提出了一种新的图像配准算法，该算法能够适应局部大的非线性变形。该算法在最小化变换之间的反向一致性误差的同时，并行估计一对图像之间的正向和反向变换。它假设要配准的两幅图像包含拓扑相似的对象，并且是使用相同的成像模态采集的。通过串联一系列小变形变换来适应从一幅图像到另一幅图像的大变形变换。每个增量变换都使用线性弹性连续介质力学模型进行正则化。给出了十个二维和十二个三维磁共振图像配准实验的结果，这些实验测试了该算法在真实脑形状上的性能。对于这些实验，与粘性流体配准算法相比，二维中的反向一致性误差平均降低了50倍，三维中降低了30倍。
