基于边缘保护多尺度空间的医学图像配准方法

从尺度空间滤波的角度分析传统多分辨率配准方法存在局限性的原因。为提高配准的精度和速度，更好地避免局部极值，提出基于边缘保护多尺度空间配准的方法。这种多尺度空间基于非线性扩散模型，可以为基于互信息的配准提供丰富的空间位置信息。同时为实现全自动配准，提出自动获取非线性扩散模型中平滑参数λ的方法。实验结果表明，文中方法用于三维医学图像配准时，优于传统的多分辨率配准方法，配准结果获得更高的精度，需要较少的迭代次数，并且在传统方法发生误配时，文中方法仍可准确配准，具有较好的鲁棒性。

Multiscale Registration Based on Edge-Preserved Scale Space for Medical Images

The limitation of the conventional multiresolution registration framework is analyzed from the perspective of scale space filtering. Edge-preserved scale space is proposed for multi-scale registration to improve the accuracy and speed and avoid local extreme. The proposed framework has a good edge preserved property which provides more spatial information for mutual information based registration. To achieve automatic registration, a method is proposed to obtain the smoothing parameter λ for non-linear diffusion model. The experimental results show that the proposed framework is superior to other traditional frameworks and suitable for 3-D medical image registration. The registration results have higher accuracy with less numbers of iteration. Furthermore, when traditional frameworks fail to register the images, the proposed framework still has accurate registration results, thus the proposed framework has better robustness.