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：本文提出了一种有效的从一组平面横截面重建对象的3D系统。首先，将噪声强度图像对齐，然后将所有图像从上到下依次分割。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型半自动方法。主动轮廓模型对组织的每个小区域使用存储在纹理特征向量中的纹理信息。对于动态迭代过程，假设连续横截面之间的差异很小，我们可以将最终轮廓用作下一部分中的初始轮廓。以此方式，对所获得的平面轮廓进行进一步处理以找到拓扑对应关系。通过使用几何和拓扑信息的标准可以促进此步骤。我们的系统已成功应用于一系列636幅显微图像（厚度为7 / spl mu / m）的轮廓提取和重建过程，如视频动画所示。
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