Robotics and Advanced Manufacturing, Center for Research and Advanced Studies of the National Polytechnic Institute, 25900, Ramos Arizpe, Mexico.
Society for Cancer Research, 4144, Arlesheim, Switzerland.
Sci Rep. 2022 Sep 12;12(1):15332. doi: 10.1038/s41598-022-19217-1.
This paper introduces a deep learning based methodology for analyzing the self-assembled, fractal-like structures formed in evaporated droplets. To this end, an extensive image database of such structures of the plant extract Viscum album Quercus [Formula: see text] was used, prepared by three different mixing procedures (turbulent, laminar, and diffusion based). The proposed pattern analysis approach is based on two stages: (1) automatic selection of patches that exhibit rich texture along the database; and (2) clustering of patches in accordance with prevalent texture by means of a Dense Convolutional Neural Network. The fractality of the patterns in each cluster is verified through Local Connected Fractal Dimension histograms. Experiments with Gray-Level Co-Occurrence matrices are performed to determine the benefit of the proposed approach in comparison with well established image analysis techniques. For the investigated plant extract, significant differences were found between the production modalities; whereas the patterns obtained by laminar flow showed the highest fractal structure, the patterns obtained by the application of turbulent mixture exhibited the lowest fractality. Our approach is the first to analyze, at the pure image level, the clustering properties of regions of interest within a database of evaporated droplets. This allows a greater description and differentiation of the patterns formed through different mixing procedures.
本文提出了一种基于深度学习的方法，用于分析在蒸发液滴中形成的自组装、分形样结构。为此，使用了植物提取物槲寄生栎树[公式：见文本]的这种结构的广泛图像数据库，该数据库是通过三种不同的混合程序（湍流、层流和基于扩散）制备的。所提出的模式分析方法基于两个阶段：（1）自动选择沿数据库具有丰富纹理的斑块；（2）通过密集卷积神经网络根据流行纹理对斑块进行聚类。通过局部连接分形维数直方图验证每个聚类中模式的分形性。通过灰度共生矩阵进行实验，以确定与成熟的图像分析技术相比，所提出方法的优势。对于所研究的植物提取物，在生产模式之间发现了显著差异；而层流获得的图案具有最高的分形结构，应用湍流混合物获得的图案具有最低的分形性。我们的方法是第一个在蒸发液滴数据库的纯图像水平上分析感兴趣区域聚类特性的方法。这允许对通过不同混合程序形成的模式进行更详细的描述和区分。
