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

Abstract

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$$10^{-3}$$ 10 - 3 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.

Vortex-like vs. turbulent mixing of a Viscum album preparation affects crystalline structures formed in dried droplets

“…Deep-learning based evaluation of patterns formed in dried solution droplets has proven effective in various models [19][20][21][22][23][24] and enables fast and objective image classi cation. We show here that DEM images obtained from a Viscum album quercus L. 10 − 3 solution mixed using turbulent or laminar ow show differences from a diffusion-based mixed control when subjected to semi-and fully-automated deep-learning pattern classi cation [11,12]. In our study all applied pattern evaluation approaches used (i.e.…”

“…The results of the supervised and unsupervised pattern evaluation based on deep-learning are described in detail elsewhere [11,12].…”

Drying of bio-colloidal sessile droplets: Advances, applications, and perspectives

“…Our group has also established a new processing tool to determine the distance between the consecutive cracks [100]. The texture of the images could also be quantified using FOS and GLCM statistical parameters [95,198,199]. The FOS is based on the gray-level distribution of the pixel values without intervening in the interpixel relationships.…”

Single Phase Transition Leads to the Nanoparticle Deposition in an Evaporating Sessile Droplet

“…The progress in experimental techniques has helped improve the understanding of particle motions and deposition characteristics and also in modulating the desired deposition patterns. For example, videography and fluorescence microscopy revealed the advective motion of micron-sized particles from the center to the pinned contact line. − Advanced analytical methods have also been used to pursue the details of profiles of the patterns following the deposition of the analyte and drying of the solvent . Means of changing the patterns of deposition have also been applied in order to have uniform deposition across the liquid–solid interface. , However, it is also important to probe the depositions as the particles settle to the substrate surface.…”

“…21−23 Advanced analytical methods have also been used to pursue the details of profiles of the patterns following the deposition of the analyte and drying of the solvent. 24 Means of changing the patterns of deposition have also been applied in order to have uniform deposition across the liquid−solid interface. 20,25 However, it is also important to probe the depositions as the particles settle to the substrate surface.…”