SIFT and Shape Context for Feature-Based Nonlinear Registration of Thoracic CT Images. Urschler, M., Bauer, J., Ditt, H., & Bischof, H. Volume 4241 LNCS, Beichel, R. & Sonka, M., editors. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 73-84. Springer, Berlin, Heidelberg, 2006. ![Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex Nonlinear image registration is a prerequisite for various medical image analysis applications. Many data acquisition protocols suffer from problems due to breathing motion which has to be taken into account for further analysis. Intensity based nonlinear registration is often used to align differing images, however this requires a large computational effort, is sensitive to intensity variations and has problems with matching small structures. In this work a feature-based image registration method is proposed that combines runtime efficiency with good registration accuracy by making use of a fully automatic feature matching and registration approach. The algorithm stages are 3D corner detection, calculation of local (SIFT) and global (Shape Context) 3D descriptors, robust feature matching and calculation of a dense displacement field. An evaluation of the algorithm on seven synthetic and four clinical data sets is presented. The quantitative and qualitative evaluations show lower runtime and superior results when compared to the Demons algorithm. © Springer-Verlag Berlin Heidelberg 2006.
@inbook{
type = {inbook},
year = {2006},
pages = {73-84},
volume = {4241 LNCS},
websites = {http://link.springer.com/10.1007/11889762_7},
publisher = {Springer, Berlin, Heidelberg},
city = {Graz, AT},
id = {1a027814-1298-39f2-af2f-64db21b5fa93},
created = {2018-02-18T20:51:33.180Z},
file_attached = {false},
profile_id = {53d1e3c7-2f16-3c81-9a84-dccd45be4841},
last_modified = {2019-11-08T01:40:44.609Z},
read = {false},
starred = {false},
authored = {true},
confirmed = {true},
hidden = {false},
citation_key = {Urschler2006_CVAMIA},
notes = {Oral},
folder_uuids = {0ec41d70-75f1-4a99-820b-0a83ccc37f54},
private_publication = {false},
abstract = {Nonlinear image registration is a prerequisite for various medical image analysis applications. Many data acquisition protocols suffer from problems due to breathing motion which has to be taken into account for further analysis. Intensity based nonlinear registration is often used to align differing images, however this requires a large computational effort, is sensitive to intensity variations and has problems with matching small structures. In this work a feature-based image registration method is proposed that combines runtime efficiency with good registration accuracy by making use of a fully automatic feature matching and registration approach. The algorithm stages are 3D corner detection, calculation of local (SIFT) and global (Shape Context) 3D descriptors, robust feature matching and calculation of a dense displacement field. An evaluation of the algorithm on seven synthetic and four clinical data sets is presented. The quantitative and qualitative evaluations show lower runtime and superior results when compared to the Demons algorithm. © Springer-Verlag Berlin Heidelberg 2006.},
bibtype = {inbook},
author = {Urschler, Martin and Bauer, Joachim and Ditt, Hendrik and Bischof, Horst},
editor = {Beichel, Reinhard and Sonka, Milan},
doi = {10.1007/11889762_7},
chapter = {SIFT and Shape Context for Feature-Based Nonlinear Registration of Thoracic CT Images},
title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}
Downloads: 0
{"_id":"y4X4tMrYWnbHm5zzm","bibbaseid":"urschler-bauer-ditt-bischof-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2006","authorIDs":["7Kn6pNXYAbwfP2qf9","vQb2TpF7F83goeATt"],"author_short":["Urschler, M.","Bauer, J.","Ditt, H.","Bischof, H."],"bibdata":{"type":"inbook","year":"2006","pages":"73-84","volume":"4241 LNCS","websites":"http://link.springer.com/10.1007/11889762_7","publisher":"Springer, Berlin, Heidelberg","city":"Graz, AT","id":"1a027814-1298-39f2-af2f-64db21b5fa93","created":"2018-02-18T20:51:33.180Z","file_attached":false,"profile_id":"53d1e3c7-2f16-3c81-9a84-dccd45be4841","last_modified":"2019-11-08T01:40:44.609Z","read":false,"starred":false,"authored":"true","confirmed":"true","hidden":false,"citation_key":"Urschler2006_CVAMIA","notes":"Oral","folder_uuids":"0ec41d70-75f1-4a99-820b-0a83ccc37f54","private_publication":false,"abstract":"Nonlinear image registration is a prerequisite for various medical image analysis applications. Many data acquisition protocols suffer from problems due to breathing motion which has to be taken into account for further analysis. Intensity based nonlinear registration is often used to align differing images, however this requires a large computational effort, is sensitive to intensity variations and has problems with matching small structures. In this work a feature-based image registration method is proposed that combines runtime efficiency with good registration accuracy by making use of a fully automatic feature matching and registration approach. The algorithm stages are 3D corner detection, calculation of local (SIFT) and global (Shape Context) 3D descriptors, robust feature matching and calculation of a dense displacement field. An evaluation of the algorithm on seven synthetic and four clinical data sets is presented. The quantitative and qualitative evaluations show lower runtime and superior results when compared to the Demons algorithm. © Springer-Verlag Berlin Heidelberg 2006.","bibtype":"inbook","author":"Urschler, Martin and Bauer, Joachim and Ditt, Hendrik and Bischof, Horst","editor":"Beichel, Reinhard and Sonka, Milan","doi":"10.1007/11889762_7","chapter":"SIFT and Shape Context for Feature-Based Nonlinear Registration of Thoracic CT Images","title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","bibtex":"@inbook{\n type = {inbook},\n year = {2006},\n pages = {73-84},\n volume = {4241 LNCS},\n websites = {http://link.springer.com/10.1007/11889762_7},\n publisher = {Springer, Berlin, Heidelberg},\n city = {Graz, AT},\n id = {1a027814-1298-39f2-af2f-64db21b5fa93},\n created = {2018-02-18T20:51:33.180Z},\n file_attached = {false},\n profile_id = {53d1e3c7-2f16-3c81-9a84-dccd45be4841},\n last_modified = {2019-11-08T01:40:44.609Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Urschler2006_CVAMIA},\n notes = {Oral},\n folder_uuids = {0ec41d70-75f1-4a99-820b-0a83ccc37f54},\n private_publication = {false},\n abstract = {Nonlinear image registration is a prerequisite for various medical image analysis applications. Many data acquisition protocols suffer from problems due to breathing motion which has to be taken into account for further analysis. Intensity based nonlinear registration is often used to align differing images, however this requires a large computational effort, is sensitive to intensity variations and has problems with matching small structures. In this work a feature-based image registration method is proposed that combines runtime efficiency with good registration accuracy by making use of a fully automatic feature matching and registration approach. The algorithm stages are 3D corner detection, calculation of local (SIFT) and global (Shape Context) 3D descriptors, robust feature matching and calculation of a dense displacement field. An evaluation of the algorithm on seven synthetic and four clinical data sets is presented. The quantitative and qualitative evaluations show lower runtime and superior results when compared to the Demons algorithm. © Springer-Verlag Berlin Heidelberg 2006.},\n bibtype = {inbook},\n author = {Urschler, Martin and Bauer, Joachim and Ditt, Hendrik and Bischof, Horst},\n editor = {Beichel, Reinhard and Sonka, Milan},\n doi = {10.1007/11889762_7},\n chapter = {SIFT and Shape Context for Feature-Based Nonlinear Registration of Thoracic CT Images},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}","author_short":["Urschler, M.","Bauer, J.","Ditt, H.","Bischof, H."],"editor_short":["Beichel, R.","Sonka, M."],"urls":{"Website":"http://link.springer.com/10.1007/11889762_7"},"biburl":"https://bibbase.org/service/mendeley/53d1e3c7-2f16-3c81-9a84-dccd45be4841","bibbaseid":"urschler-bauer-ditt-bischof-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2006","role":"author","metadata":{"authorlinks":{"urschler, m":"https://martinurschler.blogs.auckland.ac.nz/publications/"}},"downloads":0},"bibtype":"inbook","creationDate":"2020-07-27T21:18:14.322Z","downloads":0,"keywords":[],"search_terms":["lecture","notes","computer","science","including","subseries","lecture","notes","artificial","intelligence","lecture","notes","bioinformatics","urschler","bauer","ditt","bischof"],"title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","year":2006,"biburl":"https://bibbase.org/service/mendeley/53d1e3c7-2f16-3c81-9a84-dccd45be4841","dataSources":["6jn29mijTw27iZTdN","ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ"]}