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首页> 外文会议>Conference on image processing >Automatic Bone Segmentation and Alignment From MR Knee Images
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Automatic Bone Segmentation and Alignment From MR Knee Images

机译：自动骨骼分割和来自MR膝部图像的对齐

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Automatic image analysis of magnetic resonance (MR) images of the knee is simplified by bringing the knee into a reference position. While the knee is typically put into a reference position during image acquisition, this alignment will generally not be perfect. To correct for imperfections, we propose a two-step process of bone segmentation followed by elastic tissue deformation. The approach makes use of a fully-automatic segmentation of femur and tibia from Tl and T2* images. The segmentation algorithm is based on a continuous convex optimization problem, incorporating regional, and shape information. The regional terms are included from a probabilistic viewpoint, which readily allows the inclusion of shape information. Segmentation of the outer boundary of the cortical bone is encouraged by adding simple appearance-based information to the optimization problem. The resulting segmentation without the shape alignment step is globally optimal. Standard registration is problematic for knee alignment due to the distinct physical properties of the tissues constituting the knee (bone, muscle, etc.). We therefore develop an alternative alignment approach based on a simple elastic deformation model combined with strict enforcement of similarity transforms for femur and tibia based on the obtained segmentations.
机译：通过将膝盖带入参考位置，简化了膝关节的磁共振（MR）图像的自动图像分析。虽然膝盖通常在图像采集期间放入参考位置，但这种对齐通常不会是完美的。为了纠正缺陷，我们提出了两步的骨分段过程，然后是弹性组织变形。该方法利用来自TL和T2 *图像的股骨和胫骨的全自动分割。分割算法基于连续凸优化问题，包括区域和形状信息。区域术语包括在概率观点中，该观点随之易于包含形状信息。通过将基于外观的信息添加到优化问题，鼓励皮质骨外边界的分割。没有形状对准步骤的所得到的分割是全局最佳的。由于构成膝关节（骨骼，肌肉等）的组织的不同物理性质，标准注册对于膝关节对准是有问题的。因此，我们开发了一种基于简单的弹性变形模型的替代对准方法，该方法结合了基于所获得的分割的股骨和胫骨的相似性变换的严格执行。

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