3-D Microwave imaging for breast cancer

Cheng, George G.; Zhu, Yong; Grzesik, J

doi:10.1109/eucap.2012.6206249

Cited by 5 publications

(1 citation statement)

References 5 publications

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“…While tomography-based microwave imaging (TBMI) forms the dielectric scattering map of the breast, radar-based microwave imaging (RBMI) makes use of amplitude differences in the scattering signal and provides simple and powerful reconstructed amplitude-based images of the interested region. There are various numerical studies in the literature on diagnosis, detection and imaging of breast cancer with the use of microwave imaging such as circular [21] and indirect holographic reconstruction [22], confocal microwave imaging [13], multistatic adaptive microwave imaging [11], field mapping algorithm [23], hybrid reconstruction [24] and delay and sum procedures with various alternatives [8,25]. In [21], fibroglangular tissue structures mimicking the real breast tissues are used in a rotating platform filled with impedance matching liquid, and the scattering waves acquired with the help of Vivaldi antennas are processed with the holographical imaging method.…”

Section: Introductionmentioning

confidence: 99%

A Matching-Pursuit Based Approach for Detecting and Imaging Breast Cancer Tumor

Biçer¹,

Akdağlı²,

Özdemir³

2018

PIER M

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In this study, the scattering map of the breast is reconstructed by applying the matchingpursuit algorithm (MPA) to the simulation data obtained by the monostatic inverse synthetic aperture radar (ISAR) principle, and the locations of the tumors are determined by considering the peaks on the scattering map. The MPA iteratively searches the true solution by assuming every discrete point in the solution space to be a scattering center by dividing the imaging region onto a discrete grid. In order to obtain images with better resolution, the fine granularity of the grid for accurate solutions is provided at the expense of increased processing times. First, our approach based on MPA is tested on simulated data generated by MATLAB for breast tumor detection and imaging. Perfect reconstruction for the locations of the hypothetical breast tumor points is attained. Then, a full-wave electromagnetic simulation software named CST Microwave Studio (CST MWS) is used to generate backscattered electric field data from a constructed scenario in which a tumor is located in a breast model. Next, we use the collected data from the defined scenarios as an input to our algorithm. Resultant images provide successful detection and imaging of the tumor region within the breast model. The accuracy of the MATLAB and the CST MWS simulation results demonstrate the availability of our MPA-based focusing algorithm to be used effectively in medical imaging.

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Section: Introductionmentioning

confidence: 99%

A Matching-Pursuit Based Approach for Detecting and Imaging Breast Cancer Tumor

Biçer¹,

Akdağlı²,

Özdemir³

2018

PIER M

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Through-the-wall imaging of 3D objects

Cheng¹,

Zhu²,

Grzesik³

2015

2015 IEEE International Symposium on Antennas and Propagation &Amp; USNC/URSI National Radio Science Meeting

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Microwave imaging of in vivo organs and limbs

Yang

et al. 2014

2014 IEEE Conference on Antenna Measurements &Amp; Applications (CAMA)

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We introduce an efficient microwave imaging system for medical applications, especially suited for breast cancer detection. The imaging system consists of a fixed source for transmit on one end, and a scanning, receiving sensor on the other, with the target placed in between. The image of the internal organ/limb tissue under examination is then obtained across a three-dimensional region, based upon the data collected by the scanning sensor, by use of our field mapping algorithm (FMA).Application of our FMA provides a full threedimensional image of tissue structure. Six test measurement cases are given by way of demonstrating FMA performance, with promising results evident throughout.

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3-D Microwave imaging for breast cancer

Cited by 5 publications

References 5 publications

A Matching-Pursuit Based Approach for Detecting and Imaging Breast Cancer Tumor

A Matching-Pursuit Based Approach for Detecting and Imaging Breast Cancer Tumor

Through-the-wall imaging of 3D objects

Microwave imaging of in vivo organs and limbs

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