In this study, we focus on improving the efficiency and accuracy of nonrigid multi-modality registration of medical images. In this regard, we analyze the potentials of using the point similarity measurement approach as an alternative to global computation of mutual information (MI), which is still the most renown multi-modality similarity measure. The improvement capabilities are illustrated using the popular B-spline transformation model. The proposed solution is a combination of three related improvements of the most straightforward implementation, i.e., efficient computation of the voxel displacement field, local estimation of similarity and usage of a static image intensity dependence estimate. Five image registration prototypes were implemented to show contribution and dependence of the proposed improvements. When all the proposed improvements are applied, a significant reduction of computational cost and increased accuracy are obtained. The concept offers additional improvement opportunities by incorporating prior knowledge and machine learning techniques into the static intensity dependence estimation.

中文翻译：

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使用局部相似度估计的多模态非刚性图像配准

在这项研究中，我们专注于提高医学图像非刚性多模态配准的效率和准确性。在这方面，我们分析了使用点相似性测量方法替代互信息（MI）的全局计算的潜力，这仍然是最著名的多模态相似性测量。使用流行的 B 样条变换模型说明了改进能力。所提出的解决方案是最直接实现的三个相关改进的组合，即体素位移场的有效计算、相似性的局部估计和静态图像强度依赖性估计的使用。实施了五个图像配准原型，以显示所提出改进的贡献和依赖性。当应用所有提出的改进时，可以显着降低计算成本并提高准确性。该概念通过将先验知识和机器学习技术结合到静态强度依赖性估计中，提供了额外的改进机会。