A Demons algorithm for image registration with locally adaptive regularization.
Thirion's Demons is a popular algorithm for nonrigid image registration because of its linear computational complexity and ease of implementation. It approximately solves the diffusion registration problem by successively estimating force vectors that drive the deformation toward alignment and...
| Main Authors: | , , |
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| Formato: | Journal article |
| Idioma: | English |
| Publicado em: |
2009
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| _version_ | 1826302736366305280 |
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| author | Cahill, N Noble, J Hawkes, D |
| author_facet | Cahill, N Noble, J Hawkes, D |
| author_sort | Cahill, N |
| collection | OXFORD |
| description | Thirion's Demons is a popular algorithm for nonrigid image registration because of its linear computational complexity and ease of implementation. It approximately solves the diffusion registration problem by successively estimating force vectors that drive the deformation toward alignment and smoothing the force vectors by Gaussian convolution. In this article, we show how the Demons algorithm can be generalized to allow image-driven locally adaptive regularization in a manner that preserves both the linear complexity and ease of implementation of the original Demons algorithm. We show that the proposed algorithm exhibits lower target registration error and requires less computational effort than the original Demons algorithm on the registration of serial chest CT scans of patients with lung nodules. |
| first_indexed | 2024-03-07T05:52:05Z |
| format | Journal article |
| id | oxford-uuid:e93d5220-484c-4ba1-98d7-30ffbc5e4882 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:52:05Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | oxford-uuid:e93d5220-484c-4ba1-98d7-30ffbc5e48822022-03-27T10:52:48ZA Demons algorithm for image registration with locally adaptive regularization.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e93d5220-484c-4ba1-98d7-30ffbc5e4882EnglishSymplectic Elements at Oxford2009Cahill, NNoble, JHawkes, DThirion's Demons is a popular algorithm for nonrigid image registration because of its linear computational complexity and ease of implementation. It approximately solves the diffusion registration problem by successively estimating force vectors that drive the deformation toward alignment and smoothing the force vectors by Gaussian convolution. In this article, we show how the Demons algorithm can be generalized to allow image-driven locally adaptive regularization in a manner that preserves both the linear complexity and ease of implementation of the original Demons algorithm. We show that the proposed algorithm exhibits lower target registration error and requires less computational effort than the original Demons algorithm on the registration of serial chest CT scans of patients with lung nodules. |
| spellingShingle | Cahill, N Noble, J Hawkes, D A Demons algorithm for image registration with locally adaptive regularization. |
| title | A Demons algorithm for image registration with locally adaptive regularization. |
| title_full | A Demons algorithm for image registration with locally adaptive regularization. |
| title_fullStr | A Demons algorithm for image registration with locally adaptive regularization. |
| title_full_unstemmed | A Demons algorithm for image registration with locally adaptive regularization. |
| title_short | A Demons algorithm for image registration with locally adaptive regularization. |
| title_sort | demons algorithm for image registration with locally adaptive regularization |
| work_keys_str_mv | AT cahilln ademonsalgorithmforimageregistrationwithlocallyadaptiveregularization AT noblej ademonsalgorithmforimageregistrationwithlocallyadaptiveregularization AT hawkesd ademonsalgorithmforimageregistrationwithlocallyadaptiveregularization AT cahilln demonsalgorithmforimageregistrationwithlocallyadaptiveregularization AT noblej demonsalgorithmforimageregistrationwithlocallyadaptiveregularization AT hawkesd demonsalgorithmforimageregistrationwithlocallyadaptiveregularization |