非线性扩散图像配准

针对非刚性图像配准方法中对形变域使用全局一致正则化方法的缺点,提出了一种基于非线性扩散的非刚性图像配准方法。运用变分原理推导了该方法的数学模型,并将图像局部信息嵌入到该模型中,非线性扩散系数根据图像特征自适应调整,使得形变域的不同区域得到了不同程度的平滑。为提高配准速度,采用加性算子分裂法求解偏微分方程。采用合成图像与MRI图像进行了实验,结果表明:与全局一致正则化方法相比,自适应扩散配准算法获得了更符合物理实际的形变,保持了形变域的局部细节,具有更好的配准精度。

Non-Rigid Image Registration Based on Nonlinear Diffusion

Presents a new non-rigid image registration algorithm based on nonlinear diffusion. Firstly derivates the mathematic model of nonlinear registration with variational method. Then incorporates the local information of image into the mathematical framework and self-adjusts the diffusion coefficient according to image content. So the deformation in different regions obtains varying degrees of smoothness. In order to improve registration rate, adopts additive operator splitting method to solve the partial difference equation. Experiments with synthetic images and MRI images show that the new method achieves better performance than the non-adaptive regularization methods, preserves local details and has better registration accuracy.