Segmentation of high angular resolution diffusion MRI using sparse riemannian manifold clustering

Cetingul, H. Ertan, Wright, Margaret J., Thompson, Paul M. and Vidal, René (2014) Segmentation of high angular resolution diffusion MRI using sparse riemannian manifold clustering. IEEE Transactions on Medical Imaging, 33 2: 301-317. doi:10.1109/TMI.2013.2284360


Author Cetingul, H. Ertan
Wright, Margaret J.
Thompson, Paul M.
Vidal, René
Title Segmentation of high angular resolution diffusion MRI using sparse riemannian manifold clustering
Journal name IEEE Transactions on Medical Imaging   Check publisher's open access policy
ISSN 0278-0062
1558-254X
Publication date 2014-01-01
Year available 2013
Sub-type Article (original research)
DOI 10.1109/TMI.2013.2284360
Open Access Status Not Open Access
Volume 33
Issue 2
Start page 301
End page 317
Total pages 17
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Abstract We address the problem of segmenting high angular resolution diffusion imaging (HARDI) data into multiple regions (or fiber tracts) with distinct diffusion properties. We use the orientation distribution function (ODF) to model diffusion and cast the ODF segmentation problem as a clustering problem in the space of ODFs. Our approach integrates tools from sparse representation theory and Riemannian geometry into a graph theoretic segmentation framework. By exploiting the Riemannian properties of the space of ODFs, we learn a sparse representation for each ODF and infer the segmentation by applying spectral clustering to a similarity matrix built from these representations. In cases where regions with similar (resp. distinct) diffusion properties belong to different (resp. same) fiber tracts, we obtain the segmentation by incorporating spatial and user-specified pairwise relationships into the formulation. Experiments on synthetic data evaluate the sensitivity of our method to image noise and to the concentration parameters, and show its superior performance compared to alternative methods when analyzing complex fiber configurations. Experiments on phantom and real data demonstrate the accuracy of the proposed method in segmenting simulated fibers and white matter fiber tracts of clinical importance.
Keyword Affinity propagation
Diffusion magnetic resonance imaging (DMRI)
Graph theory
Harmonic analysis
Image segmentation
Sparsity
Subspace clustering
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID R01 EB008432
R01 HD050735
NHMRC 486682
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Psychology Publications
 
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