Recursive Detection and Analysis of Nanoparticles in Scanning Electron Microscopy Images

TL;DR

This paper introduces a Python-based computational framework for accurate detection and analysis of nanoparticles in SEM images, achieving high accuracy and identifying faint particles beyond manual labeling.

Contribution

The framework combines image processing techniques and integrates with RStudio for detailed nanoparticle analysis, offering improved detection accuracy over existing methods.

Findings

97% detection accuracy across test images

Effective identification of faint, manually unlabeled particles

High fidelity in morphological feature extraction

Abstract

In this study, we present a computational framework tailored for the precise detection and comprehensive analysis of nanoparticles within scanning electron microscopy (SEM) images. The primary objective of this framework revolves around the accurate localization of nanoparticle coordinates, accompanied by secondary objectives encompassing the extraction of pertinent morphological attributes including area, orientation, brightness, and length. Constructed leveraging the robust image processing capabilities of Python, particularly harnessing libraries such as OpenCV, SciPy, and Scikit-Image, the framework employs an amalgamation of techniques, including thresholding, dilating, and eroding, to enhance the fidelity of image processing outcomes. The ensuing nanoparticle data is seamlessly integrated into the RStudio environment to facilitate meticulous post-processing analysis. This encompasses a comprehensive evaluation of model accuracy, discernment of feature distribution patterns, and the identification of intricate particle arrangements. The finalized framework exhibits high nanoparticle identification within the primary sample image and boasts 97\% accuracy in detecting particles across five distinct test images drawn from a SEM nanoparticle dataset. Furthermore, the framework demonstrates the capability to discern nanoparticles of faint intensity, eluding manual labeling within the control group.