Abstract
In this paper we propose a novel non-Gaussian MRF for regularization of tensor fields for fiber tract enhancement. Two entities are considered in the model, namely, the linear component of the tensor, i.e., how much line-like the tensor is, and the angle of the eigenvector associated to the largest eigenvalue. A novel, to the best of the author’s knowledge, angular density function has been proposed. Closed form expressions of the posterior densities are obtained. Some experiments are also presented for which color-coded images are visually meaningful. Finally, a quantitative measure of regularization is also calculated to validate the achieved results based on an averaged measure of entropy.
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Keywords
- Large Eigenvalue
- Linear Component
- Brain White Matter
- Gaussian Noise Model
- Tensor Magnetic Resonance Image
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Martín-Fernández, M., Alberola-López, C., Ruiz-Alzola, J., Westin, CF. (2003). Regularization of Diffusion Tensor Maps Using a Non-Gaussian Markov Random Field Approach. In: Ellis, R.E., Peters, T.M. (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003. MICCAI 2003. Lecture Notes in Computer Science, vol 2879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39903-2_12
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DOI: https://doi.org/10.1007/978-3-540-39903-2_12
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