Ren Haiping, Wu Wenkai, Yang Hu, Chen Shengzu
Department of Nuclear Medicine, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100021.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2002 Dec;19(4):599-601, 610.
Maximization of mutual information is a powerful criterion for 3D medical image registration, allowing robust and fully accurate automated rigid registration of multi-modal images in a various applications. In this paper, a method based on normalized mutual information for 3D image registration was presented on the images of CT, MR and PET. Powell's direction set method and Brent's one-dimensional optimization algorithm were used as optimization strategy. A multi-resolution approach is applied to speedup the matching process. For PET images, pre-procession of segmentation was performed to reduce the background artefacts. According to the evaluation by the Vanderbilt University, Sub-voxel accuracy in multi-modality registration had been achieved with this algorithm.
互信息最大化是用于三维医学图像配准的一种强大准则，它能在各种应用中实现多模态图像的稳健且完全精确的自动刚性配准。本文针对CT、MR和PET图像，提出了一种基于归一化互信息的三维图像配准方法。采用鲍威尔方向集方法和布伦特一维优化算法作为优化策略。应用多分辨率方法来加速匹配过程。对于PET图像，进行了分割预处理以减少背景伪影。根据范德比尔特大学的评估，该算法在多模态配准中实现了亚体素精度。
