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Nonrigid registration of cardiac DSCT images by integrating intensity and point features

机译：通过整合强度和点特征对心脏DSCT图像进行非刚性配准

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In order to obtain accurate anatomical information for the whole heart, we propose a nonrigid registration method combining mutual information with the marching cubes method. Certain points are regarded as prior knowledge of the shape landmarks of cardiac structures. The registration process for the heart image can be divided into two steps: coarse alignment and accurate registration. The coarse alignment uses affine transformations to localize and center the image of the heart, and the accurate registration uses a Bspline method to constrain the deformation field. Mutual information combined with feature point pairs are used as the similarity measure function. All 15-dimensional feature descriptors are used to identify matched point pairs between marching cubes points in atlas intensity images and other points in the neighborhood of target images needing segmentation. Adaptive stochastic gradient descent optimization is used to obtain optimal registration parameters. Two groups of experiments show that the proposed method achieves higher registration accuracy than traditional ones based only on mutual information. They indicate that accurate anatomical information of the whole cardiac structure can be obtained. (C) 2018 Published by Elsevier Ltd.
机译：为了获得整个心脏的准确解剖信息，我们提出了一种将相互信息与行进立方体方法相结合的非刚性配准方法。某些点被认为是心脏结构形状界标的先验知识。心脏图像的配准过程可以分为两个步骤：粗对准和精确配准。粗对准使用仿射变换来定位和居中心脏图像，精确配准使用Bspline方法来约束变形场。与特征点对组合的互信息用作相似性度量函数。所有15维特征描述符都用于识别地图集强度图像中的行进立方体点与需要分割的目标图像附近的其他点之间的匹配点对。自适应随机梯度下降优化用于获得最佳配准参数。两组实验表明，与仅基于互信息的传统方法相比，该方法具有更高的配准精度。他们表明可以获取整个心脏结构的准确解剖信息。 （C）2018由Elsevier Ltd.发布

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