首页> 外文会议>Image Processing pt.1; Progress in Biomedical Optics and Imaging; vol.7 no.30 >Large-scale validation of non-rigid registration algorithms for atlas-based brain image segmentation
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Large-scale validation of non-rigid registration algorithms for atlas-based brain image segmentation

机译:基于图集的脑图像分割的非刚性配准算法的大规模验证

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摘要

In this paper, we evaluate different non-rigid image registration methodologies in the context of atlas-based brain image segmentation. Three non-rigid voxel-based registration regularization schemes (viscous fluid, elastic and curvature-based registration) combined with the mutual information similarity measure are compared. We conduct large-scale atlas-based segmentation experiments on a set of 20 anatomically labelled MR brain images in order to find the optimal parameter settings for each scheme. The performance of the optimal registration schemes is evaluated in their capability of accurately segmenting 49 different brain sub-structures of varying size and shape.
机译:在本文中,我们在基于图集的脑图像分割的背景下评估了不同的非刚性图像配准方法。比较了三种基于非刚性体素的配准正则化方案(粘性流体,基于弹性和基于曲率的配准)与互信息相似性度量。我们对一组20个在解剖学上标记的MR脑图像进行了基于图集的大规模分割实验,以找到每种方案的最佳参数设置。评估最佳配准方案的性能时,可以准确地细分出49个不同大小和形状的不同脑子结构。

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