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A BINARIZATION APPROACH FOR CT-MR REGISTRATION USING NORMALIZED MUTUAL INFORMATION

机译:基于归一化互信息的CT-MR配准的二值化方法

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Maximization of mutual information is one of the most popular algorithms for three-dimensional medical image registration. In practice, the multi-resolution scheme can help to improve the optimization speed and capture range. This paper presents a binarization/multi-resolution approach for Computed Tomography (CT) and Magnetic Resonance (MR) brain image registrations based on normalized mutual information. This approach gives improved accuracy with no loss of speed. We illustrate this approach by presenting the results of tests on the data of 7 patients. Registration accuracy is evaluated by J.M. Fitzpatrick, at Vanderbilt University, as part of the Retrospective Registration Evaluation Project. We also compare its performance to results available in the literature.
机译:互信息最大化是三维医学图像配准的最流行算法之一。实际上,多分辨率方案可以帮助提高优化速度和捕获范围。本文提出了一种基于标准化互信息的计算机断层扫描(CT)和磁共振(MR)脑图像配准的二值化/多分辨率方法。这种方法在不损失速度的情况下提高了准确性。我们通过介绍7位患者数据的测试结果来说明这种方法。作为追溯注册评估项目的一部分,范德比尔特大学的J.M. Fitzpatrick评估了注册准确性。我们还将其性能与文献中的结果进行比较。

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