Estimating the concentration of gold nanoparticles incorporated on Natural Rubber membranes using Multi-Level Starlet Optimal Segmentation
This work provides a method for analyzing nanoparticle concentration in biomedical materials, but it is incremental as it adapts existing segmentation techniques to a new domain-specific dataset.
The study tackled the problem of estimating gold nanoparticle concentration on natural rubber membranes by applying Multi-Level Starlet Optimal Segmentation (MLSOS) to photomicrographs, achieving an accuracy greater than 88% for this specific application.
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.