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

Maxime Descoteaux 1, * Rachid Deriche 1
* Corresponding author
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.
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Submitted on : Monday, July 30, 2007 - 3:07:55 PM
Last modification on : Tuesday, May 4, 2021 - 2:06:01 PM
Long-term archiving on: : Tuesday, September 21, 2010 - 1:13:47 PM


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  • HAL Id : inria-00165832, version 2



Maxime Descoteaux, Rachid Deriche. Q-Ball Images Segmentation Using Region-Based Statistical Surface Evolution. [Research Report] RR-6257, INRIA. 2007, pp.25. ⟨inria-00165832v2⟩



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