Generalizable Framework for Algorithmic Interpretation of Thin Film Morphologies in Scanning Probe Images
Generalizable Framework for Algorithmic Interpretation of Thin Film Morphologies in Scanning Probe Images
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DOI：
10.1021/acs.jcim.0c00308
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发表时间：
2020-06
影响因子：
5.6
通讯作者：
Wesley K. Tatum;D. Torrejon;Patrick O’Neil;J. Onorato;Anton B. Resing;S. Holliday;Lucas Q. Flagg;D. Ginger;C. Luscombe
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文献类型：
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作者：
Wesley K. Tatum;D. Torrejon;Patrick O’Neil;J. Onorato;Anton B. Resing;S. Holliday;Lucas Q. Flagg;D. Ginger;C. Luscombe
我们描述了一个开源且适应性广泛的 Python 库，它可以识别通过扫描探针显微镜收集的图像中的形态特征和域。 π 共轭聚合物 (CP) 具有广泛的形态和特征尺寸，是评估材料形态 Python (m2py) 库的理想选择。使用纳米结构 CP 薄膜，我们演示了通用 m2py 工作流程的功能。我们应用数值方法来增强扫描探针收集的信号，然后使用主成分分析（PCA）来降低数据的维数。然后，高斯混合模型将图像中的每个像素分割成具有相似材料属性信号的相。最后，使用连通分量标记或持久性分水岭分割将相位标记的像素分组并标记为形态域。这些工具适用于任何扫描探针测量，因此 m2py 生成的标签将允许研究人员单独处理和分析图像中已识别的域。因此，允许使用定量和统计描述符（例如域的大小、分布和形状）来描述系统的形态。这些描述符将使研究人员能够定量跟踪和比较样本内部和样本之间的差异。
We describe an open-source and widely adaptable Python library that recognizes morphological features and domains in images collected via scanning probe microscopy. π-Conjugated polymers (CPs) are ideal for evaluating the Materials Morphology Python (m2py) library, because of their wide range of morphologies and feature sizes. Using thin films of nanostructured CPs, we demonstrate the functionality of a general m2py workflow. We apply numerical methods to enhance the signals collected by the scanning probe, followed by Principal Component Analysis (PCA) to reduce the dimensionality of the data. Then, a Gaussian Mixture Model segments every pixel in the image into phases, which have similar material-property signals. Finally, the phase labeled pixels are grouped and labeled as morphological domains using either connected components labeling or persistence watershed segmentation. These tools are adaptable to any scanning probe measurement, so the labels that m2py generates will allow researchers to individually address and analyze the identified domains in the image. Thus, allowing to describe the morphology of the system using quantitative and statistical descriptors such as the size, distribution, and shape of the domains. Such descriptors will enable researchers to quantitatively track and compare differences within and between samples.