The Marian Smoluchowski Institute of Physics, The Jagiellonian University, Lojasiewicza 11, PL-30348 Krakow, Poland.
The Marian Smoluchowski Institute of Physics, The Jagiellonian University, Lojasiewicza 11, PL-30348 Krakow, Poland.
Micron. 2020 Mar;130:102800. doi: 10.1016/j.micron.2019.102800. Epub 2019 Dec 11.
Analysis of microscope images is a tedious work which requires patience and time, usually done manually by the microscopist after data collection. The results obtained in such a way might be biased by the human who performed the analysis. Here we introduce an approach of automatic image analysis, which is based on locally applied Fourier Transform and Machine Learning methods. In this approach, a whole image is scanned by a local moving window with defined size and the 2D Fourier Transform is calculated for each window. Then, all the Local Fourier Transforms are fed into Machine Learning processing. Firstly, a number of components in the data is estimated from Principal Component Analysis (PCA) Scree Plot performed on the data. Secondly, the data are decomposed blindly by Non-Negative Matrix Factorization (NMF) into interpretable spatial maps (loadings) and corresponding Fourier Transforms (factors). As a result, the microscopic image is analyzed and the features on the image are automatically discovered, based on the local changes in Fourier Transform, without human bias. The user selects only a size and movement of the scanning local window which defines the final analysis resolution. This automatic approach was successfully applied to analysis of various microscopic images with and without local periodicity i.e. atomically resolved High Angle Annular Dark Field (HAADF) Scanning Transmission Electron Microscopy (STEM) image of Au nanoisland of fcc and Au hcp phases, Scanning Tunneling Microscopy (STM) image of Au-induced reconstruction on Ge(001) surface, Scanning Electron Microscopy (SEM) image of metallic nanoclusters grown on GaSb surface, and Fluorescence microscopy image of HeLa cell line of cervical cancer. The proposed approach could be used to automatically analyze the local structure of microscopic images within a time of about a minute for a single image on a modern desktop/notebook computer and it is freely available as a Python analysis notebook and Python program for batch processing.
显微镜图像分析是一项繁琐的工作，需要耐心和时间，通常由数据采集后的显微镜专家手动完成。这样得到的结果可能会受到分析人员的人为偏见的影响。在这里，我们介绍一种基于局部应用傅里叶变换和机器学习方法的自动图像分析方法。在这种方法中，整个图像通过具有定义大小的局部移动窗口进行扫描，并为每个窗口计算二维傅里叶变换。然后，将所有局部傅里叶变换输入到机器学习处理中。首先，从对数据执行的主成分分析（PCA） scree 图中估计数据中的几个分量。其次，通过非负矩阵分解（NMF）将数据盲目分解为可解释的空间图（加载）和相应的傅里叶变换（因子）。结果，基于傅里叶变换的局部变化，在没有人为偏见的情况下，对显微镜图像进行分析，并自动发现图像上的特征。用户仅选择定义最终分析分辨率的扫描局部窗口的大小和移动。这种自动方法已成功应用于各种具有和不具有局部周期性的微观图像的分析，例如原子分辨高角环形暗场（HAADF）扫描透射电子显微镜（STEM）图像的 fcc 和 Au hcp 相的 Au 纳米岛、金诱导的 Ge(001)表面重构的扫描隧道显微镜（STM）图像、GaSb 表面生长的金属纳米团簇的扫描电子显微镜（SEM）图像和宫颈癌 HeLa 细胞系的荧光显微镜图像。该方法可用于自动分析微观图像的局部结构，对于单个图像，在现代台式机/笔记本电脑上的处理时间约为一分钟，并且可以作为 Python 分析笔记本和用于批量处理的 Python 程序免费获得。
