Segmentation of scanning tunneling microscopy images using variational methods and empirical wavelets
Segmentation of scanning tunneling microscopy images using variational methods and empirical wavelets
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DOI：
10.1007/s10044-019-00824-0
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发表时间：
2020-05-01
影响因子：
3.9
通讯作者：
Weiss, Paul S.
中科院分区：
文献类型：
--
作者：
Bui, Kevin;Fauman, Jacob;Weiss, Paul S.
在纳米科学和纳米技术领域，重要的是能够化学地使表面功能化以用于各种各样的应用。扫描隧道显微镜（STM）是这一领域的重要仪器，用于测量表面结构和化学性质，分辨率优于分子。自组装经常用于产生仅在单分子厚层中重新定义表面化学的单层（Love等人，Chem Rev 105（4）：1103-1170，2005; Nuzzo和Allara，J Am Chem Soc 105（13）：4481-4483，1983; Smith等人，Prog Surf Sci 75（1）：1-68，2004）。事实上，STM图像揭示了关于自组装单分子膜结构的丰富信息，因为它们传达了所研究材料的化学和物理性质。为了帮助和增强STM和其他图像的分析（托马斯等人，ACS Nano 10（5）：5446-5451，2016;托马斯等人，ACS Nano 9（5）：4734-4742，2015），我们提出并展示了一种图像处理框架，该框架产生两种图像分割：一种是基于强度（STM图像中的表观高度），另一种是基于纹理图案。所提出的框架从卡通+纹理分解开始，将图像分成卡通和纹理分量。之后，通过局部Chan-Vese模型的修改的多相版本（Wang等人，Pattern Recognit 43（3）：603-618，2010）来分割卡通图像，而通过2D经验小波变换和聚类算法的组合来分割纹理图像。总的来说，我们提出的框架包含几个新的功能，特别是在提出一个新的应用卡通+纹理分解和经验小波变换，并在开发一个专门的框架来分割STM图像和其他数据。为了证明我们的方法的潜力，我们将其应用到原始STM图像的各种单层，并提出相应的分割结果。
In the fields of nanoscience and nanotechnology, it is important to be able to functionalize surfaces chemically for a wide variety of applications. Scanning tunneling microscopes (STMs) are important instruments in this area used to measure the surface structure and chemistry with better than molecular resolution. Self-assembly is frequently used to create monolayers that redefine the surface chemistry in just a single-molecule-thick layer (Love et al. in Chem Rev 105(4):1103-1170, 2005; Nuzzo and Allara in J Am Chem Soc 105(13):4481-4483, 1983; Smith et al. in Prog Surf Sci 75(1):1-68, 2004). Indeed, STM images reveal rich information about the structure of self-assembled monolayers since they convey chemical and physical properties of the studied material. In order to assist in and to enhance the analysis of STM and other images (Thomas et al. in ACS Nano 10(5):5446-5451, 2016; Thomas et al. in ACS Nano 9(5):4734-4742, 2015), we propose and demonstrate an image processing framework that produces two image segmentations: One is based on intensities (apparent heights in STM images) and the other is based on textural patterns. The proposed framework begins with a cartoon + texture decomposition, which separates an image into its cartoon and texture components. Afterward, the cartoon image is segmented by a modified multiphase version of the local Chan-Vese model (Wang et al. in Pattern Recognit 43(3):603-618, 2010), while the texture image is segmented by a combination of 2D empirical wavelet transform and a clustering algorithm. Overall, our proposed framework contains several new features, specifically in presenting a new application of cartoon + texture decomposition and of the empirical wavelet transforms and in developing a specialized framework to segment STM images and other data. To demonstrate the potential of our approach, we apply it to raw STM images of various monolayers and present their corresponding segmentation results.