首页> 外文期刊>Microscopy and microanalysis: The official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada >Image Segmentation for FIB-SEM Serial Sectioning of a Si/C–Graphite Composite Anode Microstructure Based on Preprocessing and Global Thresholding
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Image Segmentation for FIB-SEM Serial Sectioning of a Si/C–Graphite Composite Anode Microstructure Based on Preprocessing and Global Thresholding

机译:基于预处理和全局阈值的Si / C-石墨复合阳极微结构的FIB-SEM序列分割

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摘要

The choice of materials that constitute electrodes and the way they are interconnected, i.e., the microstructure, influences the performance of lithium-ion batteries. For batteries with high energy and power densities, the microstructure of the electrodes must be controlled during their manufacturing process. Moreover, understanding the microstructure helps in designing a high-performance, yet low-cost battery. In this study, we propose a systematic algorithm workflow for the images of the microstructure of anodes obtained from a focused ion beam scanning electron microscope (FIB-SEM). Here, we discuss the typical issues that arise in the raw FIB-SEM images and the corresponding preprocessing methods that resolve them. Next, we propose a Fourier transform-based filter that effectively reduces curtain artifacts. Also, we propose a simple, yet an effective, global-thresholding method to identify active materials and pores in the microstructure. Finally, we reconstruct the three-dimensional structures by concatenating the segmented images. The whole algorithm workflow used in this study is not fully automated and requires user interactions such as choosing the values of parameters and removing shine-through artifacts manually. However, it should be emphasized that the proposed global-thresholding method is deterministic and stable, which results in high segmentation performance for all sectioning images.
机译:构成电极的材料的选择以及它们互连的方式,即微观结构,影响锂离子电池的性能。对于具有高能量和功率密度的电池,必须在其制造过程中控制电极的微观结构。此外,了解微观结构有助于设计高性能但低成本的电池。在本研究中,我们提出了一种系统算法的工作流程,用于从聚焦离子束扫描电子显微镜(FIB-SEM)获得的阳极微观结构的图像的工作流程。在这里,我们讨论了原始FIB-SEM图像中出现的典型问题和解决它们的相应预处理方法。接下来,我们提出了一种基于傅里叶变换的滤波器,有效减少了窗帘伪影。此外,我们提出了一种简单,但有效的全球阈值的方法,用于在微观结构中识别活性材料和孔隙。最后,我们通过连接分段图像来重建三维结构。本研究中使用的整个算法工作流程不是完全自动化的,并且需要用户交互,例如选择参数的值并手动删除闪烁的伪影。但是,应该强调提出的全局阈值方法是确定性和稳定的,这导致所有切片图像的高分割性能。

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