DocumentCode :
37759
Title :
Segmentation of High Angular Resolution Diffusion MRI Using Sparse Riemannian Manifold Clustering
Author :
Cetingul, H.E. ; Wright, Margaret J. ; Thompson, P.M. ; Vidal, Rene
Author_Institution :
Imaging & Comput. Vision Technol. Field, Siemens Corp., Princeton, NJ, USA
Volume :
33
Issue :
2
fYear :
2014
fDate :
Feb. 2014
Firstpage :
301
Lastpage :
317
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.
Keywords :
biodiffusion; biomedical MRI; brain; graph theory; image denoising; image resolution; image segmentation; medical image processing; neurophysiology; phantoms; ODF segmentation problem; Riemannian geometry; complex fiber configurations; concentration parameters; distinct diffusion properties; graph theoretic segmentation framework; high angular resolution diffusion MRI segmentation; image noise; multiple regions; orientation distribution function; phantom; similarity matrix; sparse Riemannian manifold clustering; spatial pairwise relationships; spectral clustering; user-specified pairwise relationships; white matter fiber tracts; Clustering algorithms; Image segmentation; Magnetic resonance imaging; Manifolds; Measurement; Vectors; Affinity propagation; diffusion magnetic resonance imaging (DMRI); graph theory; harmonic analysis; image segmentation; sparsity; subspace clustering;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2013.2284360
Filename :
6619442
Link To Document :
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