基于互信息的N维多模医学图像配准

目前多模医学图像配准都定位在两幅图像配准的研究,很少涉及N维(3维及3维以上)图像的配准.当用扩展的N维互信息测度(E-NMIM)进行多个图像配准时,不能保证互信息(MI)值的非负性,并且运算速度慢,达不到临床要求.本文提出一种新的N维互信息测度(N-NMIM),不仅保证了MI值的非负性,而且在[1,2]有界范围内,也提高了配准的速度.通过腰椎部位的CT,T1加权的MRI和T2加权的MRI图像进行实验,验证了这种配准方法的有效性.

N-dimensional Multimodality Medical Images Registration Based on Mutual Information

At present, the multimodality medical image registration has been all confined in registering two images and rarely involved N-dimensional images (three and more than three dimensions). Using the expanded N-dimensional mutual information measure (E-NMIM) to register multiple images inefficient, and cannot meet the clinical requirement.In addition mutual information(MI) values are not necessarily nonnegative. In this paper, we introduce a new N-dimensional mutual information measure (N-NMIM), which can ensure MI values are nonnegative, bounded to range from 1 to 2. At the same time, the rate of the registration has moved up. Then this definition is tested and proved to be effective on registration of three lumbar vertebra images through simulation, including CT,T1 weighted MRI and T2 weighted MRI.