{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/92660"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/92660","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Medical image registration on tumor growth with time series","abstract":"DSpace SAF Submission Ingestion Package generated from Vireo submission #10029 on 2016-11-09 at 10:25:43","abstract_html":"DSpace SAF Submission Ingestion Package generated from Vireo submission #10029 on 2016-11-09 at 10:25:43","abstract_has_math":false,"creators":["Wang, Kuocheng"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Systems & Entrepreneurial Engr","degree_department":null,"school":null,"contributors":["Kesavadas, Thenkurussi","LaValle, Steve"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-11-10T17:49:07Z","date_published":"2016-11-10T17:49:07Z","updated_at":"2026-07-22T22:26:35Z","subjects":["Tumor registration"],"languages":["en"],"rights":["Copyright 2016 Kuocheng Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/92660","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kesavadas, Thenkurussi","LaValle, Steve"]},{"key":"dc:creator","label":"Author","values":["Wang, Kuocheng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-11-10T17:49:07Z","2016-07-21","2016-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Systems & Entrepreneurial 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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Tumor registration"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Kuocheng Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/92660"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["DSpace SAF Submission Ingestion Package generated from Vireo submission #10029 on 2016-11-09 at 10:25:43","Made available in DSpace on 2016-11-10T17:49:07Z (GMT). No. of bitstreams: 2 WANG-THESIS-2016.pdf: 4704986 bytes, checksum: be54c5c9ab7d3debcbb75dc4183f23d1 (MD5) LICENSE.txt: 4210 bytes, checksum: e848cb95584f29e834b7deec91e06bfa (MD5) Previous issue date: 2016-07-21","Data registration is a common process in medical image analysis. The goal of data registration is to solve the transformation problem with multiple images' alignment. Conventionally, diagnosing the tumors periodically requires understanding the growth and spread of tumor which is performed by doctors by visual inspections of multiple MRI scan taken over different stages in time series. Due to the misalignment of patient's posture, comparison of these multiple MRI scans is tedious. This problem is addressed often using image registration of non-rigid body. In this method the features are first extracted from the original data set. There are several features one can extract like chamfer, line, region, etc. The feature we chose for the first method was the best fit plane, the second method was the principal axis. Those results are later compared with Iterative Closest Point(ICP) method. The 2 main motivations are 1. to compare different image data set to correlate different measures of anatomical structures, 2. to aid doctors measure the change in dynamic structural patterns of tumor growth, brain development, etc. In this thesis, we present data registration using rigid body for tumor growth which was usually explore with non-rigid body registration. Furthermore, we demonstrate different methods of image registration on rigid body for a time series of tumors. The results were qualitatively compared based on template matching of time series tumor data.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-11-09 without embargo terms","The student, Kuocheng Wang, accepted the attached license on 2016-07-20 at 09:26.","The student, Kuocheng Wang, submitted this Thesis for approval on 2016-07-20 at 10:30.","This Thesis was approved for publication on 2016-07-21 at 13:35."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Medical image registration on tumor growth with time series"]}]}],"canonical_facts":{"dc:contributor":["Kesavadas, Thenkurussi","LaValle, Steve"],"dc:creator":["Wang, Kuocheng"],"dc:date":["2016-11-10T17:49:07Z","2016-07-21","2016-08"],"dc:description":["DSpace SAF Submission Ingestion Package generated from Vireo submission #10029 on 2016-11-09 at 10:25:43","Made available in DSpace on 2016-11-10T17:49:07Z (GMT). No. of bitstreams: 2 WANG-THESIS-2016.pdf: 4704986 bytes, checksum: be54c5c9ab7d3debcbb75dc4183f23d1 (MD5) LICENSE.txt: 4210 bytes, checksum: e848cb95584f29e834b7deec91e06bfa (MD5) Previous issue date: 2016-07-21","Data registration is a common process in medical image analysis. The goal of data registration is to solve the transformation problem with multiple images' alignment. Conventionally, diagnosing the tumors periodically requires understanding the growth and spread of tumor which is performed by doctors by visual inspections of multiple MRI scan taken over different stages in time series. Due to the misalignment of patient's posture, comparison of these multiple MRI scans is tedious. This problem is addressed often using image registration of non-rigid body. In this method the features are first extracted from the original data set. There are several features one can extract like chamfer, line, region, etc. The feature we chose for the first method was the best fit plane, the second method was the principal axis. Those results are later compared with Iterative Closest Point(ICP) method. The 2 main motivations are 1. to compare different image data set to correlate different measures of anatomical structures, 2. to aid doctors measure the change in dynamic structural patterns of tumor growth, brain development, etc. In this thesis, we present data registration using rigid body for tumor growth which was usually explore with non-rigid body registration. Furthermore, we demonstrate different methods of image registration on rigid body for a time series of tumors. The results were qualitatively compared based on template matching of time series tumor data.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-11-09 without embargo terms","The student, Kuocheng Wang, accepted the attached license on 2016-07-20 at 09:26.","The student, Kuocheng Wang, submitted this Thesis for approval on 2016-07-20 at 10:30.","This Thesis was approved for publication on 2016-07-21 at 13:35."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/92660"],"dc:language":["en"],"dc:rights":["Copyright 2016 Kuocheng Wang"],"dc:subject":["Tumor registration"],"dc:title":["Medical image registration on tumor growth with time series"],"dc:type":["text"],"thesis:degree_discipline":["Systems & Entrepreneurial Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:35Z"}