断层间插值是医学图像3维重建的一个重要环节。为克服现有算法带来插值图像边界模糊和计算量大等问题，提出了一种新的适合医学图像处理的自适应插值算法。该算法充分利用组织相关性和体素相关性等特点，先判断待插值点是否属于感兴趣区域，再根据曲面相似性原理确定最佳点对进行插值。实验结果表明，插值出的新图像不仅在灰度值上，而且在组织形状上更符合实际需求。此外，该算法在计算时间上比现有的同类插值算法有了很大的改善，能有效地应用于3维重建。

Interpolation of cross sectional medical images is an important procedure in 3D reconstruction．To overcome the shortcomings, which are brought by current algorithms, such as ambiguity of objects’ boundary, high cost of computing. In this paper we propose a novel adaptive image interpolation algorithm that is suitable to medical image processing．This algorithm makes good use of characteristics of anatomical structures and volume relativity and uses the following strategies: firstly to judge whether the interpolation point belongs to ROI, then to get the best point pair according to quality of curve．The experimental results show that the novel method produces new image that is in better accord with the practical demand not only in gray but anatomical structures as well．Moreover, this algorithm is more efficient than current algorithms.Therefore, our algorithm is more suitable to 3D reconstruction．