Skip to Main content Skip to Navigation
Reports

Q-Ball Images Segmentation Using Region-Based Statistical Surface Evolution

Maxime Descoteaux 1 Rachid Deriche 1
1 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique de l'École normale supérieure, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : In this article we develop a new method to segment Q-Ball imaging (QBI) 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 QBI database show that our method is reproducible, automatic and brings a strong added value to diffusion MRI segmentation.
Complete list of metadata

https://hal.inria.fr/inria-00165832
Contributor : Maxime Descoteaux <>
Submitted on : Friday, July 27, 2007 - 6:44:47 PM
Last modification on : Tuesday, May 4, 2021 - 2:06:01 PM
Long-term archiving on: : Thursday, April 8, 2010 - 7:09:54 PM

Files

seg.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00165832, version 1

Citation

Maxime Descoteaux, Rachid Deriche. Q-Ball Images Segmentation Using Region-Based Statistical Surface Evolution. [Research Report] 2007, pp.25. ⟨inria-00165832v1⟩

Share

Metrics

Record views

5

Files downloads

71