A contextual maximum likelihood framework for modeling image registration
We introduce a novel probabilistic framework for image registration. This framework considers, in contrast to previous ones, local neighborhood information. We integrate the neighborhood information into the framework by adding layers of latent random variables, characterizing the descriptive inform...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2014
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| Online Access: | http://hdl.handle.net/1721.1/86368 https://orcid.org/0000-0002-3652-1874 |
| _version_ | 1811072230722895872 |
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| author | Wachinger, Christian Navab, Nassir |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Wachinger, Christian Navab, Nassir |
| author_sort | Wachinger, Christian |
| collection | MIT |
| description | We introduce a novel probabilistic framework for image registration. This framework considers, in contrast to previous ones, local neighborhood information. We integrate the neighborhood information into the framework by adding layers of latent random variables, characterizing the descriptive information of each image. This extension has multiple advantages. It allows for a unified description of geometric and iconic registration, with the consequential analysis of similarities. It enables to arrange registration techniques in a continuum, limited by pure intensity-and feature-based registration. With this wide spectrum of techniques combined, we can model hybrid registration approaches. The probabilistic coupling allows further to deduce optimal descriptors and to model the adaptation of description layers during the process, as it is done for joint registration/segmentation. Finally, we deduce a new registration algorithm that allows for a dynamic adaptation of the description layers during the registration. Excellent results confirm the advantages of the new registration method, the major contribution of this article lies, however, in the theoretical analysis. |
| first_indexed | 2024-09-23T09:02:42Z |
| format | Article |
| id | mit-1721.1/86368 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:02:42Z |
| publishDate | 2014 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/863682022-09-30T13:01:31Z A contextual maximum likelihood framework for modeling image registration Wachinger, Christian Navab, Nassir Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Wachinger, Christian Wachinger, Christian We introduce a novel probabilistic framework for image registration. This framework considers, in contrast to previous ones, local neighborhood information. We integrate the neighborhood information into the framework by adding layers of latent random variables, characterizing the descriptive information of each image. This extension has multiple advantages. It allows for a unified description of geometric and iconic registration, with the consequential analysis of similarities. It enables to arrange registration techniques in a continuum, limited by pure intensity-and feature-based registration. With this wide spectrum of techniques combined, we can model hybrid registration approaches. The probabilistic coupling allows further to deduce optimal descriptors and to model the adaptation of description layers during the process, as it is done for joint registration/segmentation. Finally, we deduce a new registration algorithm that allows for a dynamic adaptation of the description layers during the registration. Excellent results confirm the advantages of the new registration method, the major contribution of this article lies, however, in the theoretical analysis. 2014-05-02T15:29:06Z 2014-05-02T15:29:06Z 2012-06 Article http://purl.org/eprint/type/ConferencePaper 978-1-4673-1228-8 978-1-4673-1226-4 978-1-4673-1227-1 http://hdl.handle.net/1721.1/86368 Wachinger, C., and N. Navab. “A Contextual Maximum Likelihood Framework for Modeling Image Registration.” 2012 IEEE Conference on Computer Vision and Pattern Recognition (n.d.). https://orcid.org/0000-0002-3652-1874 en_US http://dx.doi.org/10.1109/CVPR.2012.6247902 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers (IEEE) Wachinger |
| spellingShingle | Wachinger, Christian Navab, Nassir A contextual maximum likelihood framework for modeling image registration |
| title | A contextual maximum likelihood framework for modeling image registration |
| title_full | A contextual maximum likelihood framework for modeling image registration |
| title_fullStr | A contextual maximum likelihood framework for modeling image registration |
| title_full_unstemmed | A contextual maximum likelihood framework for modeling image registration |
| title_short | A contextual maximum likelihood framework for modeling image registration |
| title_sort | contextual maximum likelihood framework for modeling image registration |
| url | http://hdl.handle.net/1721.1/86368 https://orcid.org/0000-0002-3652-1874 |
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