中文摘要：

局部结构的尺度信息是医学图像配准中一个重要的参数。然而，现有的图像配准研究大多没有关注对图像局部结构尺度的选择。该文提出了一种数据驱动的局部结构尺度选择方法，通过基于最小描述长度准则的后验概率最大化和基于马尔科夫随机场模型的空间一致性约束，从各向异性的离散尺度空间中，为每一个经超像素分割得到的局部结构分配最佳的尺度，以满足后续非刚性医学图像配准中对局部结构尺度信息的需求。

英文摘要：

The scale of local structure is a key parameter in medical image registration. Unfortunately, no much attention has been paid to the scale selection for the local structures in the images. This paper proposes a data-driven scale selection method for local structures in the image. By using minimal description length criterion to maximize the posterior probability of local structure region with coherence constraint based on the Markov random field model, an optimal scale for each local structure, which is segmented with super-pixel representation, is assigned in terms of variance in a discrete anisotropic scale space. Therefore, the local structure’s scale can be selected for further non-rigid medical image registration.