Download Medical Image Computing and Computer-Assisted Intervention - by Nicholas Ayache, Sebastien Ourselin, Anthony Maeder PDF

By Nicholas Ayache, Sebastien Ourselin, Anthony Maeder

This name is a part of a two-volume set that represent the refereed court cases of the tenth foreign convention on clinical photo Computing and Computer-Assisted Intervention, MICCAI 2007. assurance during this first quantity comprises diffusion tensor imaging and computing, cardiac imaging and robotics, photo segmentation and category, snapshot guided intervention and robotics, cutting edge scientific and organic purposes, mind atlas computing, and simulation of therapy.

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Read or Download Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007: 10th International Conference, Brisbane, Australia, October 29 - November 2, ... Vision, Pattern Recognition, and Graphics) PDF

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Additional info for Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007: 10th International Conference, Brisbane, Australia, October 29 - November 2, ... Vision, Pattern Recognition, and Graphics)

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Figure 3 shows the simulated distribution of FA, trace, and the Frobenius norm of the difference between the estimated and true tensor using a 60 direction protocol. Weighted least squares and maximum likelihood perform similarly, while linear least squares has more variability, non-linear least squares tends to have a lower estimate of trace. The simulations demonstrate the bias due to orientation of tensor derived measures when using protocols with a minimal number of gradient directions. Furthermore, when protocols with many gradient directions are used linear least squares estimation can increase variability, and non-linear least squares estimation can underestimate trace.

Tensor contraction √ Sym3 , and the norm is |D| = D : D. The tensor A can be decomposed into isotropic and deviatoric parts, defined by A = tr(A)I/3 and A = A − A, respectively. I is the identity tensor. SO3 is the group of rotations on R3 . The group action ψ : SO3 × Sym3 → Sym3 , ψ(R, D) = RDRT defines a mapping on Geodesic-Loxodromes for Diffusion Tensor Interpolation 3 Sym3 for each rotation R in SO3 . The orbit of D is SO3 (D) = {RDRT |R ∈ SO3 }; informally it is all possible rotations of D. The orbits of ψ partition Sym3 into equivalence classes because SO3 is a group.

Angelos Barmpoutis, Baba C. Vemuri, and John R. Forder 908 Non-rigid Image Registration Using Graph-Cuts . . . . . . . . . . . H. S. Chung 916 Probabilistic Speckle Decorrelation for 3D Ultrasound . . . . . . . . Catherine Laporte and Tal Arbel 925 De-enhancing the Dynamic Contrast-Enhanced Breast MRI for Robust Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuanjie Zheng, Jingyi Yu, Chandra Kambhamettu, Sarah Englander, Mitchell D.

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