Crouzier Loïc, Delvallée Alexandra, Ducourtieux Sébastien, Devoille Laurent, Tromas Christophe, Feltin Nicolas
Laboratoire National de métrologie et d'Essais - Nanometrology, 29 avenue Hennequin, 78197 Trappes Cedex, France; Institut Pprime Département Physique et Mécanique des Matériaux - 11 Bd Marie et Pierre Curie, 86962 Futuroscope Chasseneuil, France.
Laboratoire National de métrologie et d'Essais - Nanometrology, 29 avenue Hennequin, 78197 Trappes Cedex, France.
Ultramicroscopy. 2019 Dec;207:112847. doi: 10.1016/j.ultramic.2019.112847. Epub 2019 Sep 25.
Scanning Electron Microscopy (SEM) is considered as a reference technique for the determination of nanoparticle (NP) dimensional properties. Nevertheless, the image analysis is a critical step of SEM measuring process and the initial segmentation phase consisting in determining the contour of each nano-object to be measured must be correctly carried out in order to identify all pixels belonging to it. Several techniques can be applied to extract NP from SEM images and evaluate their diameter like thresholding or watershed. However, due to the lack of reference nanomaterials, few papers deals with the uncertainty associated with these segmentation methods. This article proposes a novel approach to extract the NP boundaries from SEM images using a remarkable point. The method is based on the observation that, by varying the electron beam size, the secondary electron profiles crosses each other at this point. First, a theoretical study has been performed using Monte Carlo simulation on silica NP to evaluate the robustness of the method compared with more conventional segmentation techniques (Active Contour or binarization at Full Width at Half-Maximum, FWHM). The simulation results show especially a systematic discrepancy between the NP real size and the measurements performed with both conventional methods. Moreover, generated errors are NP size-dependent. By contrast, it has been demonstrated that a very good agreement between measured and simulated diameters has been obtained with this new technique. As an example, this method of the remarkable point has been applied on SEM images of silica particles. The quality of the segmentation has been shown on silica reference nanoparticles by measuring the modal equivalent projected area diameter and comparing with calibration certificate. The results show that the NP contour can be very accurately delimited with using this point. The measurement uncertainty has been also reduced from 4.3 nm (k = 2) with conventional methods to 2.6 nm (k = 2) using the remarkable point.
扫描电子显微镜（SEM）被视为测定纳米颗粒（NP）尺寸特性的参考技术。然而，图像分析是SEM测量过程中的关键步骤，初始分割阶段（即确定每个待测纳米物体的轮廓）必须正确执行，以便识别属于该物体的所有像素。有几种技术可用于从SEM图像中提取NP并评估其直径，如阈值处理或分水岭算法。然而，由于缺乏参考纳米材料，很少有论文探讨这些分割方法的不确定性。本文提出了一种利用显著点从SEM图像中提取NP边界的新方法。该方法基于这样的观察：通过改变电子束尺寸，二次电子轮廓在该点相互交叉。首先，使用蒙特卡罗模拟对二氧化硅NP进行了理论研究，以评估该方法与更传统的分割技术（活动轮廓或半高宽处的二值化，FWHM）相比的稳健性。模拟结果尤其表明，NP实际尺寸与两种传统方法进行的测量之间存在系统差异。此外，产生的误差与NP尺寸有关。相比之下，已经证明使用这种新技术在测量直径和模拟直径之间取得了非常好的一致性。例如，这种显著点方法已应用于二氧化硅颗粒的SEM图像。通过测量模态等效投影面积直径并与校准证书进行比较，在二氧化硅参考纳米颗粒上显示了分割质量。结果表明，使用这一点可以非常准确地界定NP轮廓。测量不确定度也从传统方法的4.3 nm（k = 2）降低到使用显著点的2.6 nm（k = 2）。
