Name
Title | ||
|---|---|---|
Prediction coefficient estimation in Markov random fields for iterative X-ray CT reconstruction |
Abstract | ||
|---|---|---|
Bayesian estimation is a statistical approach for incorporating prior information through the choice of an a priori distribution for a random field. A priori image models in Bayesian image estimation are typically low-order Markov random fields (MRFs), effectively penalizing only differences among immediately neighboring voxels. This limits spectral description to a crude low-pass model. For applications where more flexibility in spectral response is desired, potential benefit exists in models which accord higher a priori probability to content in higher frequencies. Our research explores the potential of larger neighborhoods in MRFs to raise the number of degrees of freedom in spectral description. Similarly to classical filter design, the MRF coefficients may be chosen to yield a desired pass-band/stop-band characteristic shape in the a priori model of the images. In this paper, we present an alternative design method, where high-quality sample images are used to estimate the MRF coefficients by fitting them into the spatial correlation of the given ensemble. This method allows us to choose weights that increase the probability of occurrence of strong components at particular spatial frequencies. This allows direct adaptation of the MRFs for different tissue types based on sample images with different frequency content. In this paper, we consider particularly the preservation of detail in bone structure in X-ray CT. Our results show that MRF design can be used to obtain bone emphasis similar to that of conventional filtered back-projection (FBP) with a bone kernel. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1117/12.912425 | Proceedings of SPIE |
Keywords | Field | DocType |
Markov random fields,a priori density,CT reconstruction,Bayesian estimation | A priori probability,Kernel (linear algebra),Computer vision,Spatial correlation,Random field,A priori and a posteriori,Markov chain,Artificial intelligence,Bayes estimator,Physics,Bayesian probability | Conference |
Volume | ISSN | Citations |
8314 | 0277-786X | 2 |
PageRank | References | Authors |
0.46 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| jiao wang | 1 | 2 | 0.46 |
| Ken D. Sauer | 2 | 576 | 90.54 |
| Jean-Baptiste Thibault | 3 | 40 | 6.78 |
| Zhou Yu | 4 | 43 | 3.48 |
| Charles A. Bouman | 5 | 2740 | 473.62 |