TITLE:
A Semi-Vectorial Morphological Segmentation Multi-Component Images of Coumarins on Thin Layer Combined with Laser for Better Separation
AUTHORS:
Theodore Guié Toa Bi, Marcelin Sandjé, Régnima G. Oscar, Sie Ouattara, Alain Clement
KEYWORDS:
Identification, Thin Layer, Secondary Metabolites, Coumarins, Image Acquisition Segmentation, Standard Deviation, Entropy, Average Color, Algorithm, Matlab
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.12 No.6,
June
30,
2022
ABSTRACT: In this work, we propose an approach for the separation of coumarins from
thin-layer morphological segmentation based on the acquisition of
multicomponent images integrating different types of coumarins. The first step
is to make a segmentation by region, by thresholding, by contour, etc. of each
component of the digital image. Then, we proceeded to the calculations of
parameters of the regions such as the color standard deviation, the color
entropy, the average color of the pixels, the eccentricity from an algorithm on
the matlab software. The mean color values atR = 91.20 in red, atB =
213.21 in blue showed the presence of samidin in the extract. The color entropy
values HG = 5.25 in green
and HB = 4.04 in blue also
show the presence of visnadine in the leaves of Desmodium adscendens. These
values are used to consolidate the database of separation and discrimination of
the types of coumarins. The relevance of our coumarin separation or coumarin recognition method
has been highlighted compared to other methods, such as the one based on the
calculation of frontal ratios which cannot discriminate between two coumarins
having the same frontal ratio. The robustness of our method is proven with
respect to the separation and identification of some coumarins, in particular samidin
and anglicine.