Microwave imaging based on compressed sensing using adaptive thresholding

Azghani, Masoumeh; Kosmas, Panagiotis; Marvasti, Farokh

doi:10.1109/eucap.2014.6901855

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…Usually a MWI system is made of an array of antennas that illuminate the breast and collect the energy scattered by the latter. Then, a measurement chain converts this energy into data that are fed to an inverse scattering or a radar‐based algorithm in charge of building up a map of the dielectric permittivity within the breast .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a tumor detection microwave system with a realistic breast phantom

Κουτσουπίδου

Karanasiou

Kakoyiannis

et al. 2016

Micro & Optical Tech Letters

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In this article, we describe recent advances regarding a novel microwave imaging system intended to be used for in‐depth inspection of breast tissues. The system is made of a circular five‐element antenna array surrounding the breast that can be moved axially to perform a scan of the entire breast. The performance of the system is studied, both numerically and experimentally with a realistic anthropomorphic breast phantom, as a proof of concept of its fast and accurate screening ability to detect tumor‐like inclusions. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 59:6–10, 2017

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Section: Introductionmentioning

confidence: 99%

Evaluation of a tumor detection microwave system with a realistic breast phantom

Κουτσουπίδου

Karanasiou

Kakoyiannis

et al. 2016

Micro & Optical Tech Letters

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“…2, which also shows the distributions estimated by the sparsity-based IMATCS algorithm. The figure demonstrates that the IMATCS-DBIM algorithm manages to reconstruct both tumors, while traditional L2-based algorithms result in a blurred image of only one scatterer [6]. …”

Section: Sparsity-based Reconstructions Inside a Homogeneous Breasmentioning

confidence: 96%

“…The IMATCS takes advantage of an adaptive thresholding procedure with a threshold initial predefined value that decreases exponentially at each iteration [6].…”

Section: Sparsity Reconstructions With Adaptive Thresholdingmentioning

confidence: 99%

Novel inversion tools to improve performance of the DBIM algorithm for microwave medical imaging

Kosmas

Miao

2014

2014 IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applicati

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This paper presents recent results from the application of new linear inversion methods to the Distorted Born Iterative Method (DBIM) for quantitative microwave breast imaging. We first review a recently proposed method based on a regularization by projection scheme, which exploits wavelets to accommodate the trade off between reducing the number of unknowns and representing highly heterogeneous breast compositions. Second, we review a compressed sensing recovery method that can be used to improve microwave imaging resolution when sparsity can be argued in the reconstruction process. Finally, we present the application of a two-step approach for the solution of the linear problem at each DBIM, which results in fast and accurate reconstructions. We note that the presented results from all these methods cannot be obtained from traditional DBIM approaches.Index Terms-Microwave tomography, wavelet basis, compressed sensing, iterative thresholding, breast imaging, inverse scattering.

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“…In [14], and [15], we have seen that IMATCS can outperform Lasso in some scenarios like dealing with missing data. IMAT has been shown to be profitable as in microwave imaging [16]. In general, ierative methods are a large class of CS methods [17], from which we are focusing on IMAT.…”

Section: Lassomentioning

confidence: 99%

Iterative null space projection method with adaptive thresholding in sparse signal recovery

Esmaeili

Kangarshahi

Marvasti

2018

IET signal process.

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Adaptive thresholding methods have proved to yield high Signal-to-Noise Ratio (SNR) and fast convergence for sparse signal recovery. Recently, it was observed that the robustness of a class of iterative sparse recovery algorithms such as Iterative Method with Adaptive Thresholding (IMAT) outperforms the well-known LASSO algorithm in terms of reconstruction quality, convergence speed, and the sensitivity to the noise. In this paper, we introduce a new method towards sparse signal recovery from random samples (RS) of the sensing matrix or its generalized version Compressed Sensing (CS) problem. The logic of this method is based on iterative projections of the thresholded signal onto the null-space of the sensing matrix. The simulations results reveal that the proposed method has the capability of yielding noticeable output SNR values when the number of samples approaches twice the sparsity number, while other methods fail to recover the signals when approaching this number. We have also extended our algorithm to Matrix Completion (MC) scenarios and compared its efficiency to other well-known approaches for MC in the literature.

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Microwave imaging based on compressed sensing using adaptive thresholding

Cited by 8 publications

References 9 publications

Evaluation of a tumor detection microwave system with a realistic breast phantom

Evaluation of a tumor detection microwave system with a realistic breast phantom

Novel inversion tools to improve performance of the DBIM algorithm for microwave medical imaging

Iterative null space projection method with adaptive thresholding in sparse signal recovery

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