{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/40694"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/40694","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"Automatic Arterial Wall Detection and Diameter Tracking Using M-mode Ultrasound","abstract":"In this research, an algorithm to automatically detect artery wall region and estimate systolic and diastolic lumen diameter of the artery is proposed using ultrasound radio-frequency signals acquired with a single ultrasound transducer in M-mode.Detection of the region containing the artery walls is obtained using a machine learning trained classifier. Artery lumen diameter is estimated using peak detection and correlation technique.The proposed algorithm is first tested on M-mode signal acquired with human in-vivo experiments using a clinical ultrasound imaging system and a wearable ultrasound sensor (WUS).The machine learning-based artery wall region classifier was able to detect the artery wall region in M-mode signals acquired from the clinical ultrasound acquisition system with an accuracy of 88.9%. In the case of data acquired from WUS, it is 88.3%.Further, the diameter estimated with the proposed technique was within the range of the average diameter of the common carotid artery observed in humans.","abstract_html":"In this research, an algorithm to automatically detect artery wall region and estimate systolic and diastolic lumen diameter of the artery is proposed using ultrasound radio-frequency signals acquired with a single ultrasound transducer in M-mode.Detection of the region containing the artery walls is obtained using a machine learning trained classifier. Artery lumen diameter is estimated using peak detection and correlation technique.The proposed algorithm is first tested on M-mode signal acquired with human in-vivo experiments using a clinical ultrasound imaging system and a wearable ultrasound sensor (WUS).The machine learning-based artery wall region classifier was able to detect the artery wall region in M-mode signals acquired from the clinical ultrasound acquisition system with an accuracy of 88.9%. In the case of data acquired from WUS, it is 88.3%.Further, the diameter estimated with the proposed technique was within the range of the average diameter of the common carotid artery observed in humans.","abstract_has_math":false,"creators":["Krishnasamy Venugopal, Shanmugaraja"],"institution":"Carleton University","degree_name":"Master of Applied Science (M.App.Sc.)","degree_level":"Master's","degree_discipline":"Engineering, Electrical and Computer","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-24T01:34:24Z","subjects":[],"languages":["en"],"rights":["Copyright © 2020 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.22215/etd/2020-13971"],"render_values":[{"text":"10.22215/etd/2020-13971","href":"https://doi.org/10.22215/etd/2020-13971","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14718/40694","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Krishnasamy Venugopal, Shanmugaraja"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-04-08T20:05:01Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-04-08T20:05:01Z"]},{"key":"dc:date.issued","label":"Date","values":["2020"]},{"key":"dc:publisher","label":"Institution","values":["Carleton University"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering, Electrical and Computer"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (M.App.Sc.)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright © 2020 the author(s). 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Artery lumen diameter is estimated using peak detection and correlation technique.The proposed algorithm is first tested on M-mode signal acquired with human in-vivo experiments using a clinical ultrasound imaging system and a wearable ultrasound sensor (WUS).The machine learning-based artery wall region classifier was able to detect the artery wall region in M-mode signals acquired from the clinical ultrasound acquisition system with an accuracy of 88.9%. In the case of data acquired from WUS, it is 88.3%.Further, the diameter estimated with the proposed technique was within the range of the average diameter of the common carotid artery observed in humans."]},{"key":"dc:title","label":"Title","values":["Automatic Arterial Wall Detection and Diameter Tracking Using M-mode Ultrasound"]}]}],"canonical_facts":{"dc:creator":["Krishnasamy Venugopal, Shanmugaraja"],"dc:date.accessioned":["2025-04-08T20:05:01Z"],"dc:date.available":["2025-04-08T20:05:01Z"],"dc:date.issued":["2020"],"dc:description.abstract":["In this research, an algorithm to automatically detect artery wall region and estimate systolic and diastolic lumen diameter of the artery is proposed using ultrasound radio-frequency signals acquired with a single ultrasound transducer in M-mode.Detection of the region containing the artery walls is obtained using a machine learning trained classifier. Artery lumen diameter is estimated using peak detection and correlation technique.The proposed algorithm is first tested on M-mode signal acquired with human in-vivo experiments using a clinical ultrasound imaging system and a wearable ultrasound sensor (WUS).The machine learning-based artery wall region classifier was able to detect the artery wall region in M-mode signals acquired from the clinical ultrasound acquisition system with an accuracy of 88.9%. In the case of data acquired from WUS, it is 88.3%.Further, the diameter estimated with the proposed technique was within the range of the average diameter of the common carotid artery observed in humans."],"dc:identifier.doi":["10.22215/etd/2020-13971"],"dc:identifier.uri":["https://hdl.handle.net/20.500.14718/40694"],"dc:language.iso":["en"],"dc:publisher":["Carleton University"],"dc:rights":["Copyright © 2020 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. 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