Estimating the concentration of gold nanoparticles incorporated on natural rubber membranes using multi-level starlet optimal segmentation

Abstract

This study consolidates Multi-Level Starlet Segmentation (MLSS) and Multi-Level Starlet Optimal Segmentation (MLSOS), techniques for photomicrograph segmentation that use starlet wavelet detail levels to separate areas of interest in an input image. Several segmentation levels can be obtained using Multi-Level Starlet Segmentation; after that, Matthews correlation coefficient (MCC) is used to choose an optimal segmentation level, giving rise to Multi-Level Starlet Optimal Segmentation. In this paper, MLSOS is employed to estimate the concentration of gold nanoparticles with diameter around 47 nm, reducted on natural rubber membranes. These samples were used on the construction of SERS/SERRS substrates and in the study of natural rubber membranes with incorporated gold nanoparticles influence on Leishmania braziliensis physiology. Precision, recall and accuracy are used to evaluate the segmentation performance, and MLSOS presents accuracy greater than 88% for this application.

Jansen‐MIDAS: A multi‐level photomicrograph segmentation software based on isotropic undecimated wavelets

“…However, it is not trivial to define the parameters necessary to use this method for substrates of organic substances. Employing scanning electron microscopy (SEM) photomicrographs, Jansen‐MIDAS was used for separating gold nanoparticles reduced on natural rubber membranes (de Siqueira, Cabrera, Pagamisse, & Job, ), thus being able to estimate the amount of synthesized gold nanoparticles contained on the surface of these samples (de Siqueira, Cabrera, Pagamisse, & Job, c). Fission tracks on the surface of epidote crystals. There are commercial systems which separate fission tracks in photomicrographs obtained from the surface of minerals.…”