基于对应点的三维医学图像相关性插值

现有的插值方法在进行医学断层图像插值时，要么不能兼顾灰度和形状的变化，要么计算量太大。为解决这一问题，文中提出一种基于对应点的三维医学图像相关性插值算法。通过对两幅断层图像进行门限分割，获得体素的分割值。在相同密度物质的区域内，采用体素的相关性来进行插值，不同密度物质区域采用缩放区域大小作为插值数据，使新的图像不仅在灰度上，而且在组织形状上，介于原来的断层图像之间，满足了医学图像插值的要求。与线性插值相比，新算法的视觉效果好，计算误差小；与小波插值相比，新算法的计算量极大地减少。插值结果可有效地应用于构建三维体模型.

Relativity Interpolation of 3-D Medical Images Based on Corresponding Points

In the case of medical image interpolation for 3D volume models, present methods either lack the capability of interpolating gray levels and shapes at the same time, or need higher computation cost. In order to solve the problem, a relativity interpolation algorithm of 3D medical images is introduced based on corresponding points. Firstly, the original images were segmented several regions by the threshold segmentation and the segmentation value of voxels could be obtained. Secondly, relativity-based interpolation was used to obtain the data in the same density matter, and the size of area was scaled as the interpolation data in the different density matter. The new image basically satisfies the requirements of medical image interpolation. Compared with linear interpolation, the proposed algorithm greatly improves the quality of image. Moreover, the new algorithm has much lower computation cost compared with wavelet-based interpolation. The interpolation can be effectivelv used to construct 3D volume models.