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Variational Bayesian inversion for microwave breast imaging
| Content Provider | Hyper Articles en Ligne (HAL) |
|---|---|
| Author | Gharsalli, Leila Ayasso, H Duchêne, Bernard Mohammad-Djafari, Ali |
| Copyright Year | 2014 |
| Abstract | Microwave imaging is considered as a nonlinear inverse scattering problem and tackled in a Bayesian estimation framework. The object under test (a breast affected by a tumor) is assumed to be composed of compact regions made of a restricted number of different homogeneous materials. This a priori knowledge is defined by a Gauss-Markov-Potts distribution. First, we express the joint posterior of all the unknowns; then, we present in detail the variational Bayesian approximation used to compute the estimators and reconstruct both permittivity and conductivity maps. This approximation consists of the best separable probability law that approximates the true posterior distribution in the Kullback-Leibler sense. This leads to an implicit parametric optimization scheme which is solved iteratively. Some preliminary results, obtained by applying the proposed method to synthetic data, are presented and compared with those obtained by means of the classical contrast source inversion method. |
| Related Links | https://centralesupelec.hal.science/hal-01211734/file/Gharsalli-CAMES-2014.pdf |
| ISSN | 22993649 |
| Issue Number | 3/4 |
| Volume Number | 21 |
| Conference Proceedings | Computer Assisted Methods in Engineering and Science |
| e-ISSN | 22993649 |
| Language | English |
| Publisher | HAL CCSD Polish Academy of Sciences |
| Publisher Date | 2014-01-01 |
| Access Restriction | Open |
| Subject Keyword | inverse scattering microwave imaging variational Bayesian approximation breast cancer detection Gauss-Markov-Potts prior |
| Content Type | Text |
| Resource Type | Conference Proceedings |
| Subject | Mechanical Engineering Computer Science Applications Physics and Astronomy Computational Mechanics |
