Integrating geometric configuration and appearance information into a unified framework for anatomical landmark localization.

ResearchSpace Repository

Show simple item record

dc.contributor.author Urschler, Martin en
dc.contributor.author Ebner, Thomas en
dc.contributor.author Štern, Darko en
dc.date.accessioned 2019-09-29T22:03:30Z en
dc.date.issued 2018-01 en
dc.identifier.citation Medical image analysis 43:23-36 Jan 2018 en
dc.identifier.issn 1361-8415 en
dc.identifier.uri http://hdl.handle.net/2292/48006 en
dc.description.abstract In approaches for automatic localization of multiple anatomical landmarks, disambiguation of locally similar structures as obtained by locally accurate candidate generation is often performed by solely including high level knowledge about geometric landmark configuration. In our novel localization approach, we propose to combine both image appearance information and geometric landmark configuration into a unified random forest framework integrated into an optimization procedure that iteratively refines joint landmark predictions by using the coordinate descent algorithm. Depending on how strong multiple landmarks are correlated in a specific localization task, this integration has the benefit that it remains flexible in deciding whether appearance information or the geometric configuration of multiple landmarks is the stronger cue for solving a localization problem both accurately and robustly. Furthermore, no preliminary choice on how to encode a graphical model describing landmark configuration has to be made. In an extensive evaluation on five challenging datasets involving different 2D and 3D imaging modalities, we show that our proposed method is widely applicable and delivers state-of-the-art results when compared to various other related methods. en
dc.format.medium Print-Electronic en
dc.language eng en
dc.relation.ispartofseries Medical image analysis en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/ en
dc.subject Humans en
dc.subject Image Interpretation, Computer-Assisted en
dc.subject Mathematics en
dc.title Integrating geometric configuration and appearance information into a unified framework for anatomical landmark localization. en
dc.type Journal Article en
dc.identifier.doi 10.1016/j.media.2017.09.003 en
pubs.begin-page 23 en
pubs.volume 43 en
dc.rights.holder Copyright: The authors en
pubs.end-page 36 en
pubs.publication-status Published en
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en
pubs.subtype Journal Article en
pubs.elements-id 776170 en
pubs.org-id Science en
pubs.org-id School of Computer Science en
dc.identifier.eissn 1361-8423 en
pubs.record-created-at-source-date 2017-10-01 en
pubs.dimensions-id 28963961 en


Files in this item

Find Full text

This item appears in the following Collection(s)

Show simple item record

https://creativecommons.org/licenses/by-nc-nd/4.0/ Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/

Share

Search ResearchSpace


Advanced Search

Browse

Statistics