Hierarchical 3D diffusion wavelet shape priors
In this paper, we propose a novel representation of prior knowledge for image segmentation, using diffusion wavelets that can reflect arbitrary continuous interdependencies in shape data. The application of diffusion wavelets has, so far, largely been confined to signal processing. In our approach,...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2010
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| Online Access: | http://hdl.handle.net/1721.1/59305 |
| _version_ | 1826196297449734144 |
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| author | Essafi, Salma Langs, Georg Paragios, Nikos |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Essafi, Salma Langs, Georg Paragios, Nikos |
| author_sort | Essafi, Salma |
| collection | MIT |
| description | In this paper, we propose a novel representation of prior knowledge for image segmentation, using diffusion wavelets that can reflect arbitrary continuous interdependencies in shape data. The application of diffusion wavelets has, so far, largely been confined to signal processing. In our approach, and in contrast to state-of-the-art methods, we optimize the coefficients, the number and the position of landmarks, and the object topology - the domain on which the wavelets are defined - during the model learning phase, in a coarse-to-fine manner. The resulting paradigm supports hierarchies both in the model and the search space, can encode complex geometric and photometric dependencies of the structure of interest, and can deal with arbitrary topologies. We report results on two challenging medical data sets, that illustrate the impact of the soft parameterization and the potential of the diffusion operator. |
| first_indexed | 2024-09-23T10:24:40Z |
| format | Article |
| id | mit-1721.1/59305 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:24:40Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/593052022-09-26T17:40:41Z Hierarchical 3D diffusion wavelet shape priors Essafi, Salma Langs, Georg Paragios, Nikos Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Langs, Georg Langs, Georg In this paper, we propose a novel representation of prior knowledge for image segmentation, using diffusion wavelets that can reflect arbitrary continuous interdependencies in shape data. The application of diffusion wavelets has, so far, largely been confined to signal processing. In our approach, and in contrast to state-of-the-art methods, we optimize the coefficients, the number and the position of landmarks, and the object topology - the domain on which the wavelets are defined - during the model learning phase, in a coarse-to-fine manner. The resulting paradigm supports hierarchies both in the model and the search space, can encode complex geometric and photometric dependencies of the structure of interest, and can deal with arbitrary topologies. We report results on two challenging medical data sets, that illustrate the impact of the soft parameterization and the potential of the diffusion operator. Association française contre les myopathies (DTIMUSCLE project) 2010-10-13T20:57:24Z 2010-10-13T20:57:24Z 2010-05 2009-09 Article http://purl.org/eprint/type/JournalArticle 978-1-4244-4420-5 1550-5499 INSPEC Accession Number: 11367826 http://hdl.handle.net/1721.1/59305 Essafi, S., G. Langs, and N. Paragios. “Hierarchical 3D diffusion wavelet shape priors.” Computer Vision, 2009 IEEE 12th International Conference on. 2009. 1717-1724. © 2010 Institute of Electrical and Electronics Engineers. en_US http://dx.doi.org/10.1109/ICCV.2009.5459385 IEEE 12th International Conference on Computer Vision, 2009 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 |
| spellingShingle | Essafi, Salma Langs, Georg Paragios, Nikos Hierarchical 3D diffusion wavelet shape priors |
| title | Hierarchical 3D diffusion wavelet shape priors |
| title_full | Hierarchical 3D diffusion wavelet shape priors |
| title_fullStr | Hierarchical 3D diffusion wavelet shape priors |
| title_full_unstemmed | Hierarchical 3D diffusion wavelet shape priors |
| title_short | Hierarchical 3D diffusion wavelet shape priors |
| title_sort | hierarchical 3d diffusion wavelet shape priors |
| url | http://hdl.handle.net/1721.1/59305 |
| work_keys_str_mv | AT essafisalma hierarchical3ddiffusionwaveletshapepriors AT langsgeorg hierarchical3ddiffusionwaveletshapepriors AT paragiosnikos hierarchical3ddiffusionwaveletshapepriors |