Nano-particle count estimation in light microscopy images

Microscopic image analysis is an emerging field which finds applications in various research domains such as electronics, optics, and biomedicine. One subfield in microscopic image analysis deals with image captures of nano-particles, which are ubiquitously used in numerous biological and chemical studies. Accurately identifying and quantifying these particles in microscopic images brings critical insights for various scientific and industrial applications. Although many studies have been performed on particle tracking using electron microscopy (EM), fewer efforts have been directed toward light microscopy. Detection and counting of nano- particles in light microscopy images pose unique challenges, due to the resolution of imaging noise of light microscopes, but are essential for downstream applications such as molecule digital resolution measurement by photonic resonator absorption microscopy (PRAM). In this paper, we demonstrate that artificial intelligence techniques can significantly enhance nano-particle counting in light microscopy, specifically in particle images collected from the photonic resonator absorption microscopy (PRAM). We formulate the counting task as a regression task and propose a framework that utilizes a deep neural network to automatically predict the nano-particles density in light microscope images, providing a more accurate and efficient solution for this challenging task.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Nguyen, Huyen

Contributors dc:contributor

• Do, Minh N.
• Cunningham, Brian T.

• Light Microscopy Image Processing
• Particle Counting
• Deep Learning
• Convolution Neural Network

Statement dc:rights

• Copyright 2025 Huyen Nguyen

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129649

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129649