Name
Title | ||
|---|---|---|
Microwave medical imaging based on sparsity and an iterative method with adaptive thresholding. |
Abstract | ||
|---|---|---|
We propose a new image recovery method to improve the resolution in microwave imaging applications. Scattered field data obtained from a simplified breast model with closely located targets is used to formulate an electromagnetic inverse scattering problem, which is then solved using the Distorted Born Iterative Method (DBIM). At each iteration of the DBIM method, an underdetermined set of linear equations is solved using our proposed sparse recovery algorithm, IMATCS. Our results demonstrate the ability of the proposed method to recover small targets in cases where traditional DBIM approaches fail. Furthermore, in order to regularize the sparse recovery algorithm, we propose a novel L(2) -based approach and prove its convergence. The simulation results indicate that the L(2)-regularized method improves the robustness of the algorithm against the ill-posed conditions of the EM inverse scattering problem. Finally, we demonstrate that the regularized IMATCS-DBIM approach leads to fast, accurate and stable reconstructions of highly dense breast compositions. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TMI.2014.2352113 | IEEE transactions on medical imaging |
Keywords | Field | DocType |
inverse scattering,microwave medical imaging,image recovery method,linear equations,image resolution,electromagnetic inverse scattering problem,microwave imaging applications,inverse problems,l2-regularized method,highly dense breast compositions,adaptive thresholding,image reconstruction,convergence,compressed sensing,simplified breast model,microwave tomography,breast imaging,scattered field data,sparsity,distorted born iterative method,biological tissues,regularized imatcs-dbim approach,sparse recovery algorithm,l2-based approach,ill-posed conditions,microwave imaging,image reconstructions,medical image processing,vectors | Iterative reconstruction,Computer vision,Mathematical optimization,Underdetermined system,Iterative method,Artificial intelligence,Inverse problem,Microwave imaging,Thresholding,Mathematics,Compressed sensing,Inverse scattering problem | Journal |
Volume | Issue | ISSN |
34 | 2 | 1558-254X |
Citations | PageRank | References |
8 | 0.79 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masoumeh Azghani | 1 | 18 | 6.17 |
| Panagiotis Kosmas | 2 | 11 | 1.23 |
| Farokh Marvasti | 3 | 573 | 72.71 |