{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1300"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1300","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Computational methods for medical image registration","abstract":"A significant amount of research has been dedicated to the improvement of techniques in the field of medical image analysis. Imaging modalities have been improved and new acquisition methods have been introduced to reveal greater anatomical detail and to allow for more information to be extracted from medical images. However, certain challenges remain when processing and analyzing information from medical images. Image registration is a technique to find a reasonable transformation that best aligns a pair or group of images. Of particular interest in this thesis is the use of image registration in three main categories: cardiac fiber atlas construction from healthy porcine hearts, motion correction in contrast-enhanced image sequences, and the development of novel computational techniques that improve the performance of existing medical image registration methods. We provide an overview of each of the problems that we tackled, followed by a discussion of the underlying motivation and theory behind the proposed methods, and extensive validations. Most importantly, this work highlights the central role that image registration plays in biomedical research - from producing clinically relevant image-based predictive models, to enabling accurate diagnosis of diseases and the analysis of treatment response.","abstract_html":"A significant amount of research has been dedicated to the improvement of techniques in the field of medical image analysis. Imaging modalities have been improved and new acquisition methods have been introduced to reveal greater anatomical detail and to allow for more information to be extracted from medical images. However, certain challenges remain when processing and analyzing information from medical images. Image registration is a technique to find a reasonable transformation that best aligns a pair or group of images. Of particular interest in this thesis is the use of image registration in three main categories: cardiac fiber atlas construction from healthy porcine hearts, motion correction in contrast-enhanced image sequences, and the development of novel computational techniques that improve the performance of existing medical image registration methods. We provide an overview of each of the problems that we tackled, followed by a discussion of the underlying motivation and theory behind the proposed methods, and extensive validations. Most importantly, this work highlights the central role that image registration plays in biomedical research - from producing clinically relevant image-based predictive models, to enabling accurate diagnosis of diseases and the analysis of treatment response.","abstract_has_math":false,"creators":["Mojica, Mia Carmela"],"institution":"University of Ontario Institute of Technology","degree_name":"Doctor of Philosophy (PhD)","degree_level":null,"degree_discipline":"Modelling and Computational Science","degree_department":null,"school":null,"contributors":[],"advisors":["Ebrahimi, Mehran"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-04-01","date_published":"2021-04-01","updated_at":"2026-07-24T05:35:38Z","subjects":["Image registration","Cardiac fiber atlas","Motion correction","Landmark detection","Contour matching"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1300","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Ebrahimi, Mehran"]},{"key":"dc:creator","label":"Author","values":["Mojica, Mia Carmela"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-05-28T18:49:41Z","2022-03-29T19:06:41Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-05-28T18:49:41Z","2022-03-29T19:06:41Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-04-01"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Modelling and Computational Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Image registration","Cardiac fiber atlas","Motion correction","Landmark detection","Contour matching"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1300"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A significant amount of research has been dedicated to the improvement of techniques in the field of medical image analysis. Imaging modalities have been improved and new acquisition methods have been introduced to reveal greater anatomical detail and to allow for more information to be extracted from medical images. However, certain challenges remain when processing and analyzing information from medical images. Image registration is a technique to find a reasonable transformation that best aligns a pair or group of images. Of particular interest in this thesis is the use of image registration in three main categories: cardiac fiber atlas construction from healthy porcine hearts, motion correction in contrast-enhanced image sequences, and the development of novel computational techniques that improve the performance of existing medical image registration methods. We provide an overview of each of the problems that we tackled, followed by a discussion of the underlying motivation and theory behind the proposed methods, and extensive validations. Most importantly, this work highlights the central role that image registration plays in biomedical research - from producing clinically relevant image-based predictive models, to enabling accurate diagnosis of diseases and the analysis of treatment response."]},{"key":"dc:title","label":"Title","values":["Computational methods for medical image registration"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ebrahimi, Mehran"],"dc:creator":["Mojica, Mia Carmela"],"dc:date.accessioned":["2021-05-28T18:49:41Z","2022-03-29T19:06:41Z"],"dc:date.available":["2021-05-28T18:49:41Z","2022-03-29T19:06:41Z"],"dc:date.issued":["2021-04-01"],"dc:description.abstract":["A significant amount of research has been dedicated to the improvement of techniques in the field of medical image analysis. Imaging modalities have been improved and new acquisition methods have been introduced to reveal greater anatomical detail and to allow for more information to be extracted from medical images. However, certain challenges remain when processing and analyzing information from medical images. Image registration is a technique to find a reasonable transformation that best aligns a pair or group of images. Of particular interest in this thesis is the use of image registration in three main categories: cardiac fiber atlas construction from healthy porcine hearts, motion correction in contrast-enhanced image sequences, and the development of novel computational techniques that improve the performance of existing medical image registration methods. We provide an overview of each of the problems that we tackled, followed by a discussion of the underlying motivation and theory behind the proposed methods, and extensive validations. Most importantly, this work highlights the central role that image registration plays in biomedical research - from producing clinically relevant image-based predictive models, to enabling accurate diagnosis of diseases and the analysis of treatment response."],"dc:identifier.uri":["https://hdl.handle.net/10155/1300"],"dc:language.iso":["en"],"dc:subject":["Image registration","Cardiac fiber atlas","Motion correction","Landmark detection","Contour matching"],"dc:title":["Computational methods for medical image registration"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Modelling and Computational Science"],"thesis:degree_name":["Doctor of Philosophy (PhD)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:38Z"}