Recursive Detection and Analysis of Nanoparticles in Scanning Electron
  Microscopy Images
Aidan S. Wright Nathaniel P. Youmans Enrique F. Valderrama Araya
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.