Publication
Morphological component analysis for sparse regularization in plane wave imaging
Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Eric Bezzam, Rafael Eduardo Carrillo Rangel, Marcel Arditi, Adrien Georges Jean Besson
2016
Article de conférence
2016
Article de conférence
Résumé
Classical ultrasound image reconstruction mainly relies on the well-known delay-and-sum (DAS) beamforming for its simplicity and real-time capability. Sparse regularization methods propose an alternative to DAS which lead to a better inversion of the ill-posed problem resulting from the acoustic wave propagation. In the following work, a new sparse regularization method is proposed which includes a component-based modelling of the radio-frequency images as well as a pointspread- function-adaptive sparsity prior. The proposed method, evaluated on the PICMUS dataset,outperforms the classical DAS in terms of contrast and resolution.
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Proximité ontologique
Concepts associés (26)
Régularisation (mathématiques)
vignette|Les courbes bleues et vertes correspondent à deux modèles differents, tous les deux étant des solutions possibles du problème consistant à décrire les coordonnées de tous les points rouges. L'application d'une régularisation favorise le modèle moins complexe correspondant à la courbe verte. Dans le domaine des mathématiques et des statistiques, et plus particulièrement dans le domaine de l'apprentissage automatique, la régularisation fait référence à un processus consistant à ajouter de l'information à un problème, s'il est mal posé ou pour éviter le surapprentissage.
Onde
vignette|Propagation d'une onde. Une onde est la propagation d'une perturbation produisant sur son passage une variation réversible des propriétés physiques locales du milieu. Elle se déplace avec une vitesse déterminée qui dépend des caractéristiques du milieu de propagation. vignette|Une vague s'écrasant sur le rivage. Il existe trois principaux types d'ondes : les ondes mécaniques se propagent à travers une matière physique dont la substance se déforme. Les forces de restauration inversent alors la déformation.
Regularized least squares
Regularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resulting solution. RLS is used for two main reasons. The first comes up when the number of variables in the linear system exceeds the number of observations. In such settings, the ordinary least-squares problem is ill-posed and is therefore impossible to fit because the associated optimization problem has infinitely many solutions.
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