Reconstruction of Compressively Sampled Ultrasound Images Using Dual Prior Information

Alin Achim (1) , Adrian Basarab (2, 3) , George Tzagkarakis (4) , Panagiotis Tsakalides (4) , Denis Kouamé (2)

1 UWE Bristol - University of the West of England [Bristol]
2 IRIT-TCI - Traitement et Compréhension d’Images
3 UT3 - Université Toulouse III - Paul Sabatier
4 ICS-FORTH - Institute of Computer Science [FORTH, Heraklion]

This paper introduces a new technique for compressive sampling reconstruction of biomedical ultrasound images that exploits two types of prior information. On the one hand, our proposed approach is based on the observation that ultrasound RF echoes are best characterised statistically using alpha-stable distributions. On the other hand, through knowledge of the acquisition process, the support of the RF echoes in the Fourier domain can be easily inferred. Together, these two facts inform an iteratively reweighted least squares (IRLS) algorithm, which is shown to outperform previously proposed reconstruction techniques, both visually and in terms of two objective evaluation measures.

Mots clés: Alpha-stable distributions; ℓp norm; Medical ultrasound; Compressive sensing

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Alin Achim, Adrian Basarab, George Tzagkarakis, Panagiotis Tsakalides, Denis Kouamé. Reconstruction of Compressively Sampled Ultrasound Images Using Dual Prior Information. IEEE International Conference on Image Processing (ICIP 2014), Oct 2014, Paris, France. pp. 1283-1286. ⟨hal-01399892⟩