Variational Bayes inference of spatial mixture models for segmentation.
Mixture models are commonly used in the statistical segmentation of images. For example, they can be used for the segmentation of structural medical images into different matter types, or of statistical parametric maps into activating and nonactivating brain regions in functional imaging. Spatial mi...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
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2006
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| _version_ | 1797080646017875968 |
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| author | Woolrich, M Behrens, T |
| author_facet | Woolrich, M Behrens, T |
| author_sort | Woolrich, M |
| collection | OXFORD |
| description | Mixture models are commonly used in the statistical segmentation of images. For example, they can be used for the segmentation of structural medical images into different matter types, or of statistical parametric maps into activating and nonactivating brain regions in functional imaging. Spatial mixture models have been developed to augment histogram information with spatial regularization using Markov random fields (MRFs). In previous work, an approximate model was developed to allow adaptive determination of the parameter controlling the strength of spatial regularization. Inference was performed using Markov Chain Monte Carlo (MCMC) sampling. However, this approach is prohibitively slow for large datasets. In this work, a more efficient inference approach is presented. This combines a variational Bayes approximation with a second-order Taylor expansion of the components of the posterior distribution, which would otherwise be intractable to Variational Bayes. This provides inference on fully adaptive spatial mixture models an order of magnitude faster than MCMC. We examine the behavior of this approach when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging statistical parametric maps. |
| first_indexed | 2024-03-07T01:03:04Z |
| format | Journal article |
| id | oxford-uuid:8a6057fe-daa2-4743-991e-e49c0aadf327 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:03:04Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | oxford-uuid:8a6057fe-daa2-4743-991e-e49c0aadf3272022-03-26T22:31:05ZVariational Bayes inference of spatial mixture models for segmentation.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:8a6057fe-daa2-4743-991e-e49c0aadf327EnglishSymplectic Elements at Oxford2006Woolrich, MBehrens, TMixture models are commonly used in the statistical segmentation of images. For example, they can be used for the segmentation of structural medical images into different matter types, or of statistical parametric maps into activating and nonactivating brain regions in functional imaging. Spatial mixture models have been developed to augment histogram information with spatial regularization using Markov random fields (MRFs). In previous work, an approximate model was developed to allow adaptive determination of the parameter controlling the strength of spatial regularization. Inference was performed using Markov Chain Monte Carlo (MCMC) sampling. However, this approach is prohibitively slow for large datasets. In this work, a more efficient inference approach is presented. This combines a variational Bayes approximation with a second-order Taylor expansion of the components of the posterior distribution, which would otherwise be intractable to Variational Bayes. This provides inference on fully adaptive spatial mixture models an order of magnitude faster than MCMC. We examine the behavior of this approach when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging statistical parametric maps. |
| spellingShingle | Woolrich, M Behrens, T Variational Bayes inference of spatial mixture models for segmentation. |
| title | Variational Bayes inference of spatial mixture models for segmentation. |
| title_full | Variational Bayes inference of spatial mixture models for segmentation. |
| title_fullStr | Variational Bayes inference of spatial mixture models for segmentation. |
| title_full_unstemmed | Variational Bayes inference of spatial mixture models for segmentation. |
| title_short | Variational Bayes inference of spatial mixture models for segmentation. |
| title_sort | variational bayes inference of spatial mixture models for segmentation |
| work_keys_str_mv | AT woolrichm variationalbayesinferenceofspatialmixturemodelsforsegmentation AT behrenst variationalbayesinferenceofspatialmixturemodelsforsegmentation |