Fast FEM-Based Non-Rigid Registration
Notice bibliographique
Résumé
In this paper, we present a fast and accurate implementation of the diffusion-based non-rigid registration algorithm. Traditionally, finite differences are used to implement registration algorithms due to their ease of implementation. However, finite differences are sensitive to noise, and they have a narrow numerical stability range. Further, finite differences employ a uniform grid. This is often not desirable in the case of registration, as finer resolution is needed to capture the displacement field in regions that have a high number of image features, as opposed to homogeneous regions with fewer features. On the other hand, the less explored Finite Element Methods are ideal for the non-rigid registration task, as they use a non-uniform discretization of the image domain, placing points based on the local image-feature information. We present such an FEM-based implementation of a popular diffusion-based registration algorithm~\cite{stefanescu04}. Originally, this algorithm was implemented using finite differences. Experimentally, we show that our implementation is much faster than the corresponding finite difference implementation, and that it achieves this In this paper, we present a fast and accurate implementation of the diffusion-based non-rigid registration algorithm. Traditionally, finite differences are used to implement registration algorithms due to their ease of implementation. However, finite differences are sensitive to noise, and they have a narrow numerical stability range. Further, finite differences employ a uniform grid. This is often not desirable in the case of registration, as finer resolution is needed to capture the displacement field in regions that have a high number of image features, as opposed to homogeneous regions with fewer features. On the other hand, the less explored Finite Element Methods are ideal for the non-rigid registration task, as they use a non-uniform discretization of the image domain, placing points based on the local image-feature information. We present such an FEM-based implementation of a popular diffusion-based registration algorithm [8]. Originally, this algorithm was implemented using finite differences. Experimentally, we show that our implementation is much faster than the corresponding finite difference implementation, and that it achieves this computational speed without compromising the accuracy of the non-rigid registration results. computational speed without compromising the accuracy of the non-rigid registration results. [8] R. Stefanescu, X. Pennec, and N. Ayache, "Grid powered nonlinear image registration with locally adaptive regularization", Medical Image Analysis, 8(3):325–342, 2004.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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