基于最大互信息的多模医学图象配准

介绍了一种基于最大互信息原理的图象配准技术，并就实施最大互信息配准法的一些重要技术问题进行了研究，其中包括不增加新数据点的格点采样子集、不产生分数灰度值的ＰＶ插值技术和出界点策略等。该方法在搜索策略上采用了无需计算梯度的Ｐｏｗｅｌｌ算法。由于计算互信息的关键技术与有效的搜索策略的结合，使得该方法能快速、准确地实现多模医学图象的配准。用该方法对７个病人的４１套ＣＴ－ＭＲ和３５套ＭＲ－ＰＥＴ３Ｄ全脑数

Multi-Modality Medical Image Registration Basedon Maximization of Mutual Information

In this paper a maximization of mutual information based multi -modality medical image registration method is described. The method presented in this paper applies mutual information to measure the information redundancy b etween the intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. MI is used as a measure o f similarity of two images. There exist many important technical issues to be so lved about the method such as how to compute MI more accurately and how to obtai n the maximization of MI, which are seldom mentioned in published papers. In thi s paper we provide some implementation issues, for example, subsampling, PV inte rpolation, outlier strategy. Powell searching algorithm is used which does not c ompute gradients. The combination of these computation techniques and searching strategy leads to a fast and accurate multi-modality image registration. The re gistration results of 3D human brain volume data of 41 CT-MR and 35 PET-MR fro m seven patients are validated to be subvoxel. The registration method is promis ing in clinical use.