An effective system for 3-D reconstruction of objects from a set of planar cross-sections is presented in this paper. First, the noisy intensity images are aligned, and consequently, all images are segmented sequentially from top to bottom. To extract contours from a series of cross-sectional images, a new semi-automatic method based on an active contour model is developed. The active contour model uses the texture information stored in texture feature vectors for each small area of tissue. For the dynamic iteration process, we can use the final contours as the initial contour in the next section, assuming that the difference between consecutive cross-sections is small. In this way, the obtained planar contours are further processed to find the topological correspondences. This step is promoted by criteria using geometrical and topological information. Our system was successfully applied to the contour extraction and reconstruction processes of a sequence of 636 microscopic images (7 /spl mu/m in thickness), as demonstrated in our video animation.
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机译：本文介绍了来自一组平面横截面的3-D对物体的有效系统。首先，嘈杂的强度图像对齐，因此，所有图像均匀地从顶部逐行分割。为了从一系列横截面图像中提取轮廓，开发了一种基于主动轮廓模型的新的半自动方法。主动轮廓模型使用纹理信息，用于每个小面积的纹理特征向量。对于动态迭代过程，我们可以使用最终轮廓作为下一节中的初始轮廓，假设连续横截面之间的差异很小。以这种方式，进一步处理所获得的平面轮廓以找到拓扑对应关系。使用几何和拓扑信息，通过标准促进该步骤。如我们的视频动画中所示，我们的系统成功地应用于636个微观图像（7 / SPL MU / M厚度）的序列的轮廓提取和重建过程。
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