Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
A variational level set approach to multiphase motion
Journal of Computational Physics
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
International Journal of Computer Vision
Maximum entropy spherical deconvolution for diffusion MRI
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Representing diffusion MRI in 5d for segmentation of white matter tracts with a level set method
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
IEEE Transactions on Image Processing
HARDI Denoising: Variational Regularization of the Spherical Apparent Diffusion Coefficient sADC
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
A Riemannian Framework for Orientation Distribution Function Computing
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
On approximation of orientation distributions by means of spherical ridgelets
IEEE Transactions on Image Processing
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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.