A Riemannian approach to diffusion tensor images segmentation.
We address the problem of the segmentation of cerebral white matter structures from diffusion tensor images. Our approach is grounded on the theoretically well-founded differential geometrical properties of the space of multivariate normal distributions. We introduce a variational formulation, in the level set framework, to estimate the optimal segmentation according to the following hypothesis: Diffusion tensors exhibit a Gaussian distribution in the different partitions. Moreover, we must respect the geometric constraints imposed by the interfaces existing among the cerebral structures and detected by the gradient of the diffusion tensor image. We validate our algorithm on synthetic data and report interesting results on real datasets. We focus on two structures of the white matter with different properties and respectively known as the corpus callosum and the corticospinal tract.
Lenglet
Christophe
C
INRIA, 2004 route des lucioles, 06902 Sophia-Antipolis, France. clenglet@sophia.inria.fr
Rousson
Mikaël
M
Deriche
Rachid
R
Faugeras
Olivier
O
Lehericy
Stéphane
S
Ugurbil
Kamil
K
D000465:Algorithms; D001185:Artificial Intelligence; D003337:Corpus Callosum / Q000033:anatomy & histology; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011712:Pyramidal Tracts / Q000033:anatomy & histology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique