Zhang Jianwei, Han Guoqiang
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2008 Feb;25(1):12-7.
Registration based on mutual information is a typical method in medical image registration. Mutual information is a common similarity measure in image registration, which has excellent robustness and accuracy, but large calculation amount makes it difficult to be applied to clinics. In this paper, a registration algorithm based on multi-resolution and hybrid optimization is adopted to implement 2-dimension monomodal and multimodal registrations of MRI and CT images of human heads with different numbers of gray bins. Results of experiments show that registration precisions have not notable change with 32, 64 gray bins, compared with 256 gray bins, whereas the computation costs decrease remarkably.
基于互信息的配准是医学图像配准中的一种典型方法。互信息是图像配准中常用的相似性度量,具有出色的鲁棒性和准确性,但计算量较大,难以应用于临床。本文采用一种基于多分辨率和混合优化的配准算法,对不同灰度级数量的人头MRI和CT图像进行二维单模态和多模态配准。实验结果表明,与256个灰度级相比,32、64个灰度级时配准精度无显著变化,而计算成本显著降低。