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Physics-based Elastic Registration Using Non-radial Basis Functions And Including Landmark Localization Uncertainties

机译:使用非径向基函数并包含地标定位不确定性的基于物理的弹性配准

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We introduce a new approximation approach for landmark-based elastic image registration using Gaussian elastic body splines (GEBS). We formulate an extended energy functional related to the Navier equation under Gaussian forces which allows to individually weight the landmarks according to their localization uncertainties. These uncertainties are characterized either by scalar weights or by weight matrices representing isotropic or anisotropic errors. Since the approach is based on a physical deformation model, cross-effects in elastic deformations can be taken into account. Moreover, with Gaussian forces we have a free parameter to control the locality of the transformation for improved registration of local geometric image differences. We demonstrate the applicability of our scheme based on analytic experiments, 3D CT images from the Truth Cube experiment, as well as 2D MR images of the brain. From the experiments it turned out that the new approximating GEBS approach achieves more accurate registration results in comparison to previously proposed interpolating GEBS as well as interpolating and approximating TPS.
机译:我们使用高斯弹性体样条(GEBS)为基于地标的弹性图像配准引入了一种新的近似方法。我们在高斯力的作用下制定了与Navier方程相关的扩展能量函数,该函数允许根据地标的定位不确定性对地标进行单独加权。这些不确定性的特征在于标量权重或代表各向同性或各向异性误差的权重矩阵。由于该方法基于物理变形模型,因此可以考虑弹性变形的交叉效应。此外,借助高斯力,我们有一个自由参数来控制变换的局部性,以改善局部几何图像差异的配准。我们基于分析实验,来自Truth Cube实验的3D CT图像以及大脑的2D MR图像证明了该方案的适用性。从实验中可以看出,与先前提出的内插GEBS以及内插和逼近TPS相比,新的近似GEBS方法可获得更准确的配准结果。

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