Interference enhanced wide-field nanoparticle imaging is a highly sensitive technique that has found numerous applications in labeled and label-free sub-diffraction-limited pathogen detection. It also provides unique opportunities for nanoparticle classification upon detection. More specif- ically, the nanoparticle defocus images result in a particle-specific response that can be of great utility for nanoparticle classification, particularly based on type and size. In this work, we com- bine a model based supervised learning algorithm with a wide-field common-path interferometric microscopy method to achieve accurate nanoparticle classification. We verify our classification schemes experimentally by using gold and polystyrene nanospheres.
@article{arxiv.1703.02997,
title = {Nanoparticle Classification in Wide-field Interferometric Microscopy by Supervised Learning from Model},
author = {Oguzhan Avci and Celalettin Yurdakul and M. Selim Unlu},
journal= {arXiv preprint arXiv:1703.02997},
year = {2017}
}