{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129649"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129649","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Nano-particle count estimation in light microscopy images","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Nguyen, Huyen"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Do, Minh N.","Cunningham, Brian T."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-07","date_published":"2025-05-07","updated_at":"2026-07-22T22:25:05Z","subjects":["Light Microscopy Image Processing","Particle Counting","Deep Learning","Convolution Neural Network"],"languages":["en","eng"],"rights":["Copyright 2025 Huyen Nguyen"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129649","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Do, Minh N.","Cunningham, Brian T."]},{"key":"dc:creator","label":"Author","values":["Nguyen, Huyen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-05-07","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Light Microscopy Image Processing","Particle Counting","Deep Learning","Convolution Neural Network"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Huyen Nguyen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129649"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Huyen Nguyen, accepted the attached license on 2025-05-07 at 12:54.","The student, Huyen Nguyen, submitted this Thesis for approval on 2025-05-07 at 13:07.","This Thesis was approved for publication on 2025-05-07 at 15:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21215 on 2025-10-19 at 19:52:02","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Nano-particle count estimation in light microscopy images"]}]}],"canonical_facts":{"dc:contributor":["Do, Minh N.","Cunningham, Brian T."],"dc:creator":["Nguyen, Huyen"],"dc:date":["2025-05-07","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Huyen Nguyen, accepted the attached license on 2025-05-07 at 12:54.","The student, Huyen Nguyen, submitted this Thesis for approval on 2025-05-07 at 13:07.","This Thesis was approved for publication on 2025-05-07 at 15:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21215 on 2025-10-19 at 19:52:02","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129649"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Huyen Nguyen"],"dc:subject":["Light Microscopy Image Processing","Particle Counting","Deep Learning","Convolution Neural Network"],"dc:title":["Nano-particle count estimation in light microscopy images"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}