X-ray computed tomography (CT) is a powerful technique for non-destructive volumetric inspection of objects and is widely used for studying internal structures of a large variety of sample types. The raw data obtained through an X-ray CT practice is a gray-scale 3D array of voxels. This data must undergo a geometric feature extraction process before it can be used for interpretation purposes. Such feature extraction process is conventionally done manually, but with the ever-increasing trend of image data sizes and the interest in identifying more miniature features, automated feature extraction methods are sought. Given the fact that conventional computer-vision-based methods, which attempt to segment images into partitions using techniques such as thresholding, are often only useful for aiding the manual feature extraction process, machine-learning based algorithms are becoming popular to develop fully automated feature extraction processes. Nevertheless, the machine-learning algorithms require a huge pool of labeled data for proper training, which is often unavailable. We propose to address this shortage, through a data synthesis procedure. We will do so by fabricating miniature features, with known geometry, position and orientation on thin silicon wafer layers using a femtosecond laser machining system, followed by stacking these layers to construct a 3D object with internal features, and finally obtaining the X-ray CT image of the resulting 3D object. Given that the exact geometry, position and orientation of the fabricated features are known, the X-ray CT image is inherently labeled and is ready to be used for training the machine learning algorithms for automated feature extraction. Through several examples, we will showcase: (1) the capability of synthesizing features of arbitrary geometries and their corresponding labeled images; and (2) use of the synthesized data for training machine-learning based shape classifiers and features parameter extractors.

X射线计算机断层扫描（CT）是一种用于对象的非破坏性体积检查的强大技术，被广泛用于研究多种样品类型的内部结构。通过X射线CT练习获得的原始数据是三维3D三维像素阵列。该数据必须经过几何特征提取过程，才能用于解释目的。这种特征提取过程通常是手动完成的，但是随着图像数据大小的不断增长的趋势以及对识别更多微型特征的兴趣，人们寻求自动特征提取方法。考虑到以下事实，即传统的基于计算机视觉的方法试图使用诸如阈值之类的技术将图像分割为多个分区，通常仅有助于辅助手动特征提取过程，基于机器学习的算法在开发全自动特征提取过程中正变得越来越流行。但是，机器学习算法需要大量的标记数据才能进行正确的训练，而这通常是不可用的。我们建议通过数据综合程序来解决这一短缺问题。我们将通过使用飞秒激光加工系统在薄硅晶片层上制造具有已知几何形状，位置和方向的微型特征，然后堆叠这些层以构造具有内部特征的3D对象，最终获得X射线CT生成的3D对象的图像。假设已知制作特征的确切几何形状，位置和方向，X射线CT图像已被固有地标记，可以用于训练机器学习算法以进行自动特征提取。通过几个示例，我们将展示：（1）综合任意几何特征及其相应标记图像的能力；（2）将合成数据用于训练基于机器学习的形状分类器和特征参数提取器。