Deformable image registration aims at estimating a proper displacement field from a fixed image and a moving one. Variational deformable registration models often consist of a data term of the images and a regularization term of the estimated displacement field. In this paper, we propose a variational model for registering uni-modal medical images with intensity biases. Precisely, the proposed model employs local correlation coefficients (LCC) as the data term and regularizes all possible displacement fields as functions of bounded deformation (BD functions), which is thus termed as BDLCC model. A primal-dual algorithm is derived for solving the model. Two conclusions can be drawn from two-dimensional and three-dimensional numerical experiments: (1) the proposed primal-dual algorithm is effective and stable, (2) the BDLCC model is effective for deformable registration of uni-modal images with intensity biases, and competitive with other state-of-the-art deformable registration models.

可变形图像配准旨在从固定图像和移动图像中估计适当的位移场。变分可变形配准模型通常由图像的数据项和估计位移场的正则化项组成。在本文中，我们提出了一种用于配准具有强度偏差的单模态医学图像的变分模型。准确地说，所提出的模型采用局部相关系数（LCC）作为数据项，并将所有可能的位移场正则化为有界变形的函数（BD 函数），因此称为 BDLCC 模型。推导出原始对偶算法来求解模型。从二维和三维数值实验可以得出两个结论：（1）提出的原始对偶算法有效且稳定，