École Polytechnique Fédérale de Lausanne (EPFL), Interdisciplinary Centre for Electron Microscopy (CIME), Lausanne, Switzerland.
Micron. 2013 Sep-Oct;52-53:49-56. doi: 10.1016/j.micron.2013.08.005. Epub 2013 Aug 31.
Acquisition of three-dimensional (3D) spectral data is nowadays common using many different microanalytical techniques. In order to proceed to the 3D reconstruction, data processing is necessary not only to deal with noisy acquisitions but also to segment the data in term of chemical composition. In this article, we demonstrate the value of multivariate statistical analysis (MSA) methods for this purpose, allowing fast and reliable results. Using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) coupled with a focused ion beam (FIB), a stack of spectrum images have been acquired on a sample produced by laser welding of a nickel-titanium wire and a stainless steel wire presenting a complex microstructure. These data have been analyzed using principal component analysis (PCA) and factor rotations. PCA allows to significantly improve the overall quality of the data, but produces abstract components. Here it is shown that rotated components can be used without prior knowledge of the sample to help the interpretation of the data, obtaining quickly qualitative mappings representative of elements or compounds found in the material. Such abundance maps can then be used to plot scatter diagrams and interactively identify the different domains in presence by defining clusters of voxels having similar compositions. Identified voxels are advantageously overlaid on secondary electron (SE) images with higher resolution in order to refine the segmentation. The 3D reconstruction can then be performed using available commercial softwares on the basis of the provided segmentation. To asses the quality of the segmentation, the results have been compared to an EDX quantification performed on the same data.
目前，许多不同的微分析技术都可以用来获取三维（3D）光谱数据。为了进行 3D 重建，不仅需要对数据进行处理以处理噪声采集，还需要根据化学成分对数据进行分割。在本文中，我们展示了多元统计分析（MSA）方法在这方面的价值，这些方法可以快速、可靠地得到结果。我们使用扫描电子显微镜（SEM）和能量色散 X 射线光谱（EDX），结合聚焦离子束（FIB），对镍钛丝和不锈钢丝激光焊接而成的样品进行了光谱图像的堆叠采集，该样品具有复杂的微观结构。我们使用主成分分析（PCA）和因子旋转对这些数据进行了分析。PCA 可以显著提高数据的整体质量，但会产生抽象的成分。在这里，我们展示了旋转后的成分可以在没有样品先验知识的情况下使用，以帮助解释数据，快速获得代表材料中元素或化合物的定性映射图。然后，可以使用这些丰度映射图绘制散点图，并通过定义具有相似成分的体素簇来交互识别存在的不同域。识别出的体素有利地在具有更高分辨率的二次电子（SE）图像上进行叠加，以细化分割。然后可以基于提供的分割，使用可用的商业软件对 3D 重建进行操作。为了评估分割的质量，我们将结果与对同一数据进行的 EDX 定量进行了比较。
