Deep learning applied to analyze patterns from evaporated droplets of Viscum album extracts.

Acuña, Carlos; Mier Y Terán, Alfonso; Kokornaczyk, Maria Olga; Baumgartner, Stephan; Castelán, Mario (2022). Deep learning applied to analyze patterns from evaporated droplets of Viscum album extracts. Scientific Reports, 12(1), p. 15332. Nature Publishing Group 10.1038/s41598-022-19217-1

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Medical Education > Institute of Complementary and Integrative Medicine (IKIM)

UniBE Contributor:

Baumgartner, Stephan

Subjects:

600 Technology > 610 Medicine & health

ISSN:

2045-2322

Publisher:

Nature Publishing Group

Language:

English

Submitter:

Pubmed Import

Date Deposited:

14 Sep 2022 11:10

Last Modified:

05 Dec 2022 16:24

Publisher DOI:

10.1038/s41598-022-19217-1

PubMed ID:

36097279

BORIS DOI:

10.48350/172872

URI:

https://boris.unibe.ch/id/eprint/172872

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