Liao Shu, Chung Albert C S
Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong.
Inf Process Med Imaging. 2009;21:163-75. doi: 10.1007/978-3-642-02498-6_14.
Non-rigid image registration is a challenging task in medical image analysis. In recent years, there are two essential issues. First, intensity similarity is not necessarily equivalent to anatomical similarity when the anatomical correspondences between subject and template images are established. Second, the registration algorithm should be robust against monotonic gray-level transformation when aligning anatomical structures in the presence of bias fields. In this paper, a new feature based non-rigid registration method is proposed to deal with these two problems. The proposed method is based on a new type of image feature, called Uniform Spherical Structure Pattern (USSP). USSP encodes voxel-wise interaction information and geometric properties of anatomical structures. It is computationally efficient, rotation invariant and theoretically monotonic gray-level transformation invariant. The USSP feature is integrated with the Markov random field (MRF) discrete labeling framework to define energy function for registration in this paper. If the segmentation results are available, explicit anatomical correspondence can be established as an additional energy term. The energy function is optimized via the alpha-expansion algorithms. The proposed method is compared with three widely used non-rigid registration methods on both simulated and real databases obtained from BrainWeb and IBSR. Experimental results demonstrate that the proposed method achieves the highest registration accuracy among all the compared methods.
非刚性图像配准是医学图像分析中的一项具有挑战性的任务。近年来，存在两个关键问题。第一，在建立主体图像与模板图像之间的解剖对应关系时，强度相似性不一定等同于解剖相似性。第二，在存在偏差场的情况下对齐解剖结构时，配准算法应能抵抗单调灰度变换。本文提出了一种基于新特征的非刚性配准方法来处理这两个问题。所提出的方法基于一种新型的图像特征，称为均匀球面结构模式（USSP）。USSP对体素级的相互作用信息和解剖结构的几何特性进行编码。它计算效率高、旋转不变且理论上对单调灰度变换不变。本文将USSP特征与马尔可夫随机场（MRF）离散标记框架相结合来定义配准的能量函数。如果有分割结果，则可以建立明确的解剖对应关系作为一个附加能量项。通过α扩展算法对能量函数进行优化。在从BrainWeb和IBSR获得的模拟数据库和真实数据库上，将所提出的方法与三种广泛使用的非刚性配准方法进行了比较。实验结果表明，在所比较的所有方法中，所提出的方法实现了最高的配准精度。
