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A method based on mutual information and gradient information for medical image registration

机译:基于互信息和梯度信息的医学图像配准方法

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摘要

Mutual information is widely used in medical image registration, because it does not require preprocessing the image. However, the local maximum problem in the registration is insurmountable. We combine mutual information and gradient information to solve this problem and apply it to the non-rigid deformation image registration. To improve the accuracy, we provide some implemental issues, for example, the Powell searching algorithm, gray interpolation and consideration of outlier points. The experimental results show the accuracy of the method and the feasibility in non-rigid medical image registration.
机译:互信息被广泛用于医学图像配准中,因为它不需要预处理图像。但是,注册中的本地最大问题是无法克服的。我们结合了互信息和梯度信息来解决该问题,并将其应用于非刚性变形图像配准。为了提高准确性,我们提供了一些实施问题,例如Powell搜索算法,灰色插值和离群点的考虑。实验结果表明了该方法的正确性和在非刚性医学图像配准中的可行性。

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