High Angular Resolution Diffusion MRI Segmentation Using Region-Based Statistical Surface Evolution

Maxime Descoteaux (1) , Rachid Deriche (2)

1 NEUROSPIN - Service NEUROSPIN
2 ODYSSEE - Computer and biological vision

In this article we develop a new method to segment high angular resolution diffusion imaging (HARDI) data. We first estimate the orientation distribution function (ODF) using a fast and robust spherical harmonic (SH) method. Then, we use a region-based statistical surface evolution on this image of ODFs to efficiently find coherent white matter fiber bundles. We show that our method is appropriate to propagate through regions of fiber crossings and we show that our results outperform state-of-the-art diffusion tensor (DT) imaging segmentation methods, inherently limited by the DT model. Results obtained on synthetic data, on a biological phantom, on real datasets and on all 13 subjects of a public NMR database show that our method is reproducible, automatic and brings a strong added value to diffusion MRI segmentation.

Mots clés: Diffusion tensor imaging (DTI); High angular resolution diffusion imaging (HARDI); Q-ball imaging (QBI); Orientation distribution function (ODF); Region-based segmentation; Level set framework

Domaines: Traitement des images [eess.IV]

Maxime Descoteaux, Rachid Deriche. High Angular Resolution Diffusion MRI Segmentation Using Region-Based Statistical Surface Evolution. Journal of Mathematical Imaging and Vision, 2008, ⟨10.1007/s10851-008-0071-8⟩. ⟨inria-00423393⟩