University of Illinois Urbana-Champaign

Nano-particle count estimation in light microscopy images

Nguyen, Huyen

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

Description

Title
Nano-particle count estimation in light microscopy images
Author(s)
Nguyen, Huyen
Issue Date
2025-05-07
Director of Research (if dissertation) or Advisor (if thesis)
Do, Minh N.
Committee Member(s)
Cunningham, Brian T.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • Light microscopy image processing
  • Particle counting
  • Deep learning
  • Convolution neural network
Abstract
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.
Graduation Semester
2025-05
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/129649
Copyright and License Information
Copyright 2025 Huyen Nguyen

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