Matrix-valued Image Processing
Topics
  • Diffusion-weighted MRI approximation using 2nd and 4th-order tensors,
  • Continuous interpolation using the corresponding Riemannian metric,
  • Tensor-field segmentation and registration.
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    Collaborators

    Saurav B. Chandra
    John R. Forder, PhD
    Dena Howland, PhD
    Minsig Hwang
    Bing Jian, PhD
    Timothy Shepherd, MD, PhD
    Baba C. Vemuri, PhD
    Selected Publications

    A. Barmpoutis, B. C. Vemuri, T. M. Shepherd, and J. R. Forder. "Tensor splines for interpolation and approximation of DT-MRI with applications to segmentation of isolated rat hippocampi. TMI: IEEE Transactions on Medical Imaging 26(11), 2007
    A. Barmpoutis, B. C. Vemuri and J. R. Forder. "Registration of High Angular Resolution Diffusion MRI Images using 4th Order Tensors". MICCAI, 2007
    A. Barmpoutis, B. Jian, B. C. Vemuri and T. M. Shepherd. "Symmetric Positive 4th Order Tensors & their Estimation from DW-MRI". IPMI, 2007
    A. Barmpoutis, and B. C. Vemuri. "Exponential Tensors: A framework for efficient higher-order DT-MRI computations". ISBI, 2007
    A. Barmpoutis, B. C. Vemuri, and J. Forder. "Robust Tensor Splines for Approximation of Diffusion Tensor MRI Data". MMBIA-CVPR, 2006

    Click here for abstracts, BibTex entries, and PDFs.
    Demo

    This animation shows a real data example from an isolated rat hippocampus. In the beginning of the animation the original noisy tensor field is shown. As a result the estimated fiber tracts are also noisy.

    Then, the tensor field is shown after having fitted a tensor spline to the data. Now the noise in the field has been succesfully removed and the computed fibers are more coherent.

    Click here for more experimental results, discussions, plots etc.

    Click here for free software, on-line applets, source code, etc.