College of Life Science and Technology, Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel). 2013 Jun 13;13(6):7599-617. doi: 10.3390/s130607599.
Non-rigid multi-modal image registration plays an important role in medical image processing and analysis. Existing image registration methods based on similarity metrics such as mutual information (MI) and sum of squared differences (SSD) cannot achieve either high registration accuracy or high registration efficiency. To address this problem, we propose a novel two phase non-rigid multi-modal image registration method by combining Weber local descriptor (WLD) based similarity metrics with the normalized mutual information (NMI) using the diffeomorphic free-form deformation (FFD) model. The first phase aims at recovering the large deformation component using the WLD based non-local SSD (wldNSSD) or weighted structural similarity (wldWSSIM). Based on the output of the former phase, the second phase is focused on getting accurate transformation parameters related to the small deformation using the NMI. Extensive experiments on T1, T2 and PD weighted MR images demonstrate that the proposed wldNSSD-NMI or wldWSSIM-NMI method outperforms the registration methods based on the NMI, the conditional mutual information (CMI), the SSD on entropy images (ESSD) and the ESSD-NMI in terms of registration accuracy and computation efficiency.
非刚性多模态图像配准在医学图像处理和分析中起着重要作用。现有的基于相似性度量（如互信息（MI）和平方和差（SSD））的图像配准方法，要么无法达到高精度，要么无法达到高效率。为了解决这个问题，我们提出了一种新的两阶段非刚性多模态图像配准方法，该方法将基于 Weber 局部描述符（WLD）的相似性度量与基于正则化互信息（NMI）的归一化互信息（NMI）相结合，使用可变形自由形态变形（FFD）模型。第一阶段旨在使用基于 WLD 的非局部 SSD（wldNSSD）或加权结构相似性（wldWSSIM）恢复大变形分量。在前一阶段的输出基础上，第二阶段专注于使用 NMI 获得与小变形相关的准确变换参数。对 T1、T2 和 PD 加权磁共振图像的广泛实验表明，所提出的 wldNSSD-NMI 或 wldWSSIM-NMI 方法在配准精度和计算效率方面优于基于 NMI、条件互信息（CMI）、基于熵图像的 SSD（ESSD）和 ESSD-NMI 的配准方法。
