DTI segmentation by statistical surface evolution

We address the problem of the segmentation of cerebral white matter structures from diffusion tensor images (DTI). A DTI produces, from a set of diffusion-weighted MR images, tensor-valued images where each voxel is assigned with a 3 times 3 symmetric, positive-definite matrix. This second order ten...

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Veröffentlicht in:	IEEE transactions on medical imaging 2006-06, Vol.25 (6), p.685-700
Hauptverfasser:	Lenglet, C., Rousson, M., Deriche, R.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial Intelligence Brain - cytology Covariance matrix Data Interpretation, Statistical Diffusion Magnetic Resonance Imaging - methods Diffusion tensor imaging Diffusion tensor MRI Euclidean distance Fisher information matrix Gaussian distribution Gaussian processes Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image segmentation Imaging, Three-Dimensional - methods information geometry Information Storage and Retrieval - methods Kullback-Leibler divergence level-set Models, Neurological Models, Statistical Nerve Fibers, Myelinated - ultrastructure Pattern Recognition, Automated - methods Probability probability metric Reproducibility of Results Riemannian geometry segmentation Sensitivity and Specificity Statistical distributions Studies Symmetric matrices Tensile stress
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