Incorporation of Landmark Error Ellipsoids for Image Registration Based on Approximating Thin-Plate Splines
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چکیده
Nonrigid registration of 3D tomographic images of di erent modalities (e.g., CT and MR images) is a key issue for neurosurgery and radiotherapy planning. Here, we consider image registration based on a set of corresponding anatomical point landmarks and approximating thin-plate splines. With this approach it is possible to take into account landmark localization errors and thus to control the in uence of the landmarks on the registration result. This is important for practical applications since landmark extraction is always prone to error, either in the case of interactive or automatic landmark localization. Our approximating thin-plate spline approach is an extension of the original interpolating thin-plate spline approach [1],[3] and has been introduced in [5],[6] for images of arbitrary dimension. The approach is based on functional analysis (regularization theory) and uses scalar weights to represent landmark localization errors. For a di erent approach to relax the interpolation condition see [2]. However, this approach has been not been related to a minimizing functional. Also, this approach has only been described for the 2D case and has only been applied to synthetic data. One problem with our approach in [5],[6] is that scalar weights for the landmarks are only a coarse characterization of the localization errors. Generally, the errors are di erent in di erent directions and thus are anisotropic. Another problem is how to acquire the additional information about the landmark errors. Here, one approach is the utilization of prior knowledge about the localization errors of anatomical landmarks. Another possibility is to infer such information by analyzing the local intensity variations of the image. In this contribution, we further extend our approach by incorporating covariance matrices of landmark position errors. For 3D images we have 3 3 covariance matrices and
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تاریخ انتشار 1998