当切片间距离较大时，如何快速，准确地重构出复杂器官的表面并显示它，在医学临床应用中已变得越来越重要。本文首先提出了一种保持轮廓形状特征的自适应特征点采样算法，该算法大大减少了后续处理的存储量和计算量，其次，在于对图象特性的分析，提出了一种特殊点匹配算法，该算法考虑到了特征点在原图象上的灰度，梯度幅值，梯度方向和位置对匹配的影响，提高了重建表面的准确度。实验结果证明了上述算法的有效性。

How to fast and accurately reconstruct complex surfaces of organs has become increasingly important while the interslice distance is much larger than the interpixel distance. First, this paper presents a shape-keeping and adaptive feature points sampling algorithm, which can reduce storage and computational requirments in the post-processing procedure. Second, an algorithm for reconstructing surfaces from CT cross-sections based on shape feature point matching is presented, which takes into account four properties of tomographic images, i. e. intensity,gradient magnitude, gradient direction, and position disparity of the feature points to improve the accduracy of the reconstructed surface. The experimental results show the effectiveness of the above algorithms.