Segmentation of scanning electron microscopy images from natural rubber samples with gold nanoparticles using starlet wavelets

TL;DR

This paper introduces a starlet wavelet-based algorithm for segmenting scanning electron microscopy images, specifically to locate gold nanoparticles in natural rubber, achieving over 85% accuracy and aiding future material analysis.

Contribution

The paper presents a novel segmentation method using starlet wavelets tailored for SEM images of rubber with gold nanoparticles, improving accuracy and enabling detailed nanoparticle analysis.

Findings

Achieved over 85% accuracy in nanoparticle segmentation

Demonstrated effectiveness of starlet wavelets for SEM image analysis

Facilitated future quantitative studies of nanoparticle distribution

Abstract

Electronic microscopy has been used for morphology evaluation of different materials structures. However, microscopy results may be affected by several factors. Image processing methods can be used to correct and improve the quality of these results. In this paper we propose an algorithm based on starlets to perform the segmentation of scanning electron microscopy images. An application is presented in order to locate gold nanoparticles in natural rubber membranes. In this application, our method showed accuracy greater than 85% for all test images. Results given by this method will be used in future studies, to computationally estimate the density distribution of gold nanoparticles in natural rubber samples and to predict reduction kinetics of gold nanoparticles at different time periods.