Estimating the concentration of gold nanoparticles incorporated on Natural Rubber membranes using Multi-Level Starlet Optimal Segmentation

TL;DR

This paper introduces MLSOS, a segmentation method combining starlet wavelet analysis and MCC, to accurately estimate gold nanoparticle concentration on natural rubber membranes, aiding biomedical applications.

Contribution

The paper presents MLSOS, a novel segmentation approach that optimally identifies gold nanoparticles in images, improving accuracy over previous methods.

Findings

MLSOS achieves over 88% accuracy in nanoparticle segmentation.

The method effectively estimates nanoparticle concentration on rubber membranes.

Segmentation performance evaluated using precision, recall, and accuracy.

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.