On Single-Image Super-Resolution in 3D Brain Magnetic Resonance Imaging

Farah Bazzi (1) , Muriel Mescam (2) , Adrian Basarab (1, 3) , Denis Kouamé (1)

1 IRIT-MINDS - CoMputational imagINg anD viSion
2 CERCO - Centre de recherche cerveau et cognition
3 UT3 - Université Toulouse III - Paul Sabatier

The objective of this work is to apply 3D super resolution (SR) techniques to brain magnetic resonance (MR) image restoration. Two 3D SR methods are considered following different trends: one recently proposed tensor-based approach and one inverse problem algorithm based on total variation and low rank regularization. The evaluation of their effectiveness is assessed through the segmentation of brain compartments: gray matter, white matter and cerebrospinal fluid. The two algorithms are qualitatively and quantitatively evaluated on simulated images with ground truth available and on experimental data. The originality of this work is to consider the SR methods as an initial step towards the final segmentation task. The results show the ability of both methods to overcome the loss of spatial resolution and to facilitate the segmentation of brain structures with improved accuracy compared to native low-resolution MR images. Both algorithms achieved almost equivalent results with a highly reduced computational time cost for the tensor-based approach.

Mots clés: Single image super-resolution; Structural MRI; Segmentation; Tensors; Total variation regularization; Low rank

Domaines: Traitement du signal et de l'image [eess.SP]

Farah Bazzi, Muriel Mescam, Adrian Basarab, Denis Kouamé. On Single-Image Super-Resolution in 3D Brain Magnetic Resonance Imaging. 41st IEEE Annual International Conference on Engineering in Medicine and Biology (EMBC 2019), Jul 2019, Berlin, Germany. pp.0, ⟨10.1109/EMBC.2019.8857959⟩. ⟨hal-02891677⟩