Diffusion-based spatial priors for imaging

@inproceedings{Harrison2007DiffusionbasedSP,
  title={Diffusion-based spatial priors for imaging},
  author={Lee M. Harrison and William D. Penny and John Ashburner and Nelson J. Trujillo-Barreto and Karl J. Friston},
  booktitle={NeuroImage},
  year={2007}
}
We describe a Bayesian scheme to analyze images, which uses spatial priors encoded by a diffusion kernel, based on a weighted graph Laplacian. This provides a general framework to formulate a spatial model, whose parameters can be optimized. The application we have in mind is a spatiotemporal model for imaging data. We illustrate the method on a random effects analysis of fMRI contrast images from multiple subjects; this simplifies exposition of the model and enables a clear description of its… CONTINUE READING

Citations

Publications citing this paper.
Showing 1-10 of 39 extracted citations

Classification of Motor Imagery Tasks in Source Domain

2018 IEEE International Conference on Mechatronics and Automation (ICMA) • 2018
View 3 Excerpts
Highly Influenced

Bayesian spatio-temporal decomposition for electromagnetic imaging of extended sources based on Destrieux atlas

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) • 2018
View 2 Excerpts

Probabilistic non-linear registration with spatially adaptive regularisation

M. J. Cardosoa, M. Modata, +4 authors S. Ourselina
2017
View 1 Excerpt

References

Publications referenced by this paper.
Showing 1-10 of 59 references

Gaussian Processes for Machine Learning

Advanced Lectures on Machine Learning • 2009
View 11 Excerpts
Highly Influenced

A general framework for low level vision

IEEE Trans. Image Processing • 1998
View 4 Excerpts
Highly Influenced

Spectral Graph Theory. American Mathematics Society, Providence Rhode Island

F. Chung
1991
View 12 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…