Papp Laszlo, Zsoter Norbert, Szabo Gergely, Bejan Csaba, Szimjanovszki Emil, Zuhayra Maaz
Nuclear Medicine Department, UK-SH Campus Kiel, Christian Albrechts University of Kiel, D 24105, Germany.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:5825-8. doi: 10.1109/IEMBS.2009.5335168.
A method is proposed to register three multimodal medical data, where none of the images are superimposed. Contrary to previously presented solutions that perform more simultaneous registrations after one-by-one, present method registers all images in parallel. The method minimizes the registration error by seeking the optimum of a vector including rigid transformation parameters of both reslice images. To measure the similarity among all three images, a higher dimensional extended normalized mutual information have been adopted. Comparison with simultaneous methods have been performed on brain and femoral multi-modal image triples. Based on the comparative results, presented parallel method significantly outperforms the simultaneous methods in both translation and rotation registration error minimizations. On the contrary, the simultaneous methods need less computational time to converge.
提出了一种用于配准三个多模态医学数据的方法，其中没有图像相互叠加。与之前逐个进行更多同步配准的解决方案不同，本方法并行配准所有图像。该方法通过寻找包含两个重切片图像刚性变换参数的向量的最优值来最小化配准误差。为了测量所有三个图像之间的相似性，采用了更高维度的扩展归一化互信息。已在脑和股骨多模态图像三元组上与同步方法进行了比较。基于比较结果，所提出的并行方法在平移和旋转配准误差最小化方面均显著优于同步方法。相反，同步方法收敛所需的计算时间更少。
