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Fully automatic 3D feature-based registration of multi-modality medical images

机译:基于全自动3D特征的多模态医学图像配准

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In this paper, we present an automated multi-modality registration algorithm based on hierarchical feature extraction. The approach, which has not been used previously, can be divided into two distinct stages f feature extraction (edge detection, surface extraction), and geometric matching. Two kinds of corresponding features -- edge and surface -- are extracted hierarchically from various image modalities. The registration then is performed using least--squares matching of the automatically extracted features. Both the robustness and accuracy of feature extraction and geometric matching steps are evaluated using simulated and patient images. The preliminary results show the error is on the average of one voxel. We have shown the proposed 3D registration algorithm provides a simple and fast method for automatic registering of MR-to-CT and MR-to-PET image modalities. Our results are comparable to other techniques and require no user interaction.
机译:在本文中,我们提出了一种基于分层特征提取的自动多模态注册算法。该方法以前没有使用过,可以分为特征提取(边缘检测,表面提取)和几何匹配两个不同阶段。从各种图像模态中分层提取了两种相应的特征-边缘和曲面。然后使用自动提取的特征的最小二乘匹配执行配准。特征提取的稳健性和准确性以及几何匹配步骤均使用模拟图像和患者图像进行评估。初步结果表明,该误差平均为一个体素。我们已经显示了提出的3D配准算法为MR-to-CT和MR-to-PET图像模态的自动配准提供了一种简单而快速的方法。我们的结果可与其他技术媲美,不需要用户交互。

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