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

• Published in: Journal of mathematical imaging and vision Vol. 33; no. 2; pp. 239 - 252
• Main Authors: Descoteaux, Maxime, Deriche, Rachid
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.02.2009; Springer Verlag
• Subjects: Applications of Mathematics; Computer Science; Image Processing; Image Processing and Computer Vision; Mathematical Methods in Physics; Signal,Image and Speech Processing; High angular resolution diffusion imaging (HARDI); Orientation distribution function (ODF); Level set framework; Q-ball imaging (QBI); Diffusion tensor imaging (DTI); Region-based segmentation
• ISSN: 0924-9907; 1573-7683
• DOI: 10.1007/s10851-008-0071-8

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.