Nanoparticle Classification in Wide-field Interferometric Microscopy by Supervised Learning from Model

Oguzhan Avci; Celalettin Yurdakul; M. Selim Unlu

doi:10.1364/ao.56.004238

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arXiv · 1703.02997

Nanoparticle Classification in Wide-field Interferometric Microscopy by Supervised Learning from Model

Oguzhan Avci·Celalettin Yurdakul·M. Selim Unlu

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Abstract

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.

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Oguzhan Avci, Celalettin Yurdakul, M. Selim Unlu. 2017-03-08. Nanoparticle Classification in Wide-field Interferometric Microscopy by Supervised Learning from Model. https://doi.org/10.1364/ao.56.004238

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