Microwave imaging of breast cancer tumor inside voxel-based breast phantom using conformal antennas

Unal, Ilhami; Turetken, Bahattin; Cotur, Yasin

doi:10.1109/ursigass.2014.6930132

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…Various imaging techniques and algorithms are there to identify the depth of the tumor. Microwave Imaging via Space-Time (MIST) beamforming algorithm [7], Parallel plate waveguide port technique [8], Time of Arrival (TOA) data fusion method [9,10], Delay and Sum (DAS) beamforming algorithm [11], etc. can be used to detect the depth of the tumor.…”

Section: Introductionmentioning

confidence: 99%

Detection of Depth of the Tumor in Microwave Imaging Using Ground Penetrating Radar Algorithm

Selvaraj¹,

Sheela²,

Krishnan³

et al. 2020

PIER M

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Microwave Imaging (MI) is a new technique for detecting breast cancer using electrical property difference between the non-malignant and malignant tissues present in the breast. Numerous studies show that detecting the depth of the tumor is the essential measure in determining additional management. Developing evidence in many of the literature surveys illustrate that detecting tumor depth is a precise parameter for identifying the affected area. Thus, Ground Penetrating Radar (GPR) algorithm is applied successfully to detect the exact depth of the malignant tissue. Generally, GPR is originally conceived for archaeological investigations, building condition assessment, detection of buried mines, etc. But here an effort has been made to apply GPR to Radar-based breast cancer detection. The simulated bandwidth of the proposed UWB antenna starts at 2.4 GHz and ends at 4.7 GHz. The electromagnetic wave reflected due to dielectric property variation is used by GPR algorithm to identify the depth of the tumor. Before applying a depth migration technique, preprocessing steps like Cartesian form transformation, Hermitian Signal Processing, and Inverse Fast Fourier Transform (IFFT) have to be followed in the backscattered signal to convert positive frequency data into time-domain data. Depth details can be noticed in the migrated image, after applying the migration procedure. Results show that GPR algorithm can be effectively used for detecting the tumor embedded in the depth of the breast tissue. To understand the effectiveness of this imaging scheme, an experimental analysis is done using a combination of wheat flour and water-petroleum jelly. The measured impedance bandwidth of the UWB antenna ranges from 2.8 GHz to 4.48 GHz. The observation is done for a known spherical tumor of diameter 13 mm which is placed at different depths from the skin layer. While applying the algorithm in the received backscattered signal, we were able to detect correctly the tumor at a depth of 45 mm embedded in the breast tissue. The experimental results are compared with simulation ones to validate the aptness of a microwave imaging approach for detecting the depth of the tumor.

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Section: Introductionmentioning

confidence: 99%

Detection of Depth of the Tumor in Microwave Imaging Using Ground Penetrating Radar Algorithm

Selvaraj¹,

Sheela²,

Krishnan³

et al. 2020

PIER M

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show abstract

“…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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“…In RBMWI, a scattering map of the measured tissue is generated. There are various studies on TBMWI and RBMWI in the literature such as confocal MWI, statistical MWI, matching‐pursuit‐based imaging, dynamic MWI, MUSIC‐inspired imaging, asynchronous particle swarm optimization and dynamic differential evolution‐based imaging, circular, and indirect holographic imaging, delay and sum procedures and its various alternatives …”

Section: Introductionmentioning

confidence: 99%

“…There are various studies on TBMWI and RBMWI in the literature such as confocal MWI, 7-9 statistical MWI, 10 matchingpursuit-based imaging, 11,12 dynamic MWI, 13 MUSICinspired imaging, 14,15 asynchronous particle swarm optimization and dynamic differential evolution-based imaging, 16 circular, 17 and indirect holographic imaging, 18,19 delay and sum procedures and its various alternatives. 9,20 . Unal et al 20 placed a spherical tumor model with 2 mm diameter at the depth of 40 mm inside the voxel-based breast phantom and the imaged the obtained simulation data using delay-and-sum algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of the inverse circular radon transform-based imaging approach for breast cancer screening

Biçer

Akdağlı

2018

Int J RF Microw Comput Aided Eng

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In this article, the approach based on the inverse circular radon transform technique (ICRT) is proposed to obtain the image of the breast cancer screening based on microwaves. A simplified numerical breast model is constructed and computational results are obtained with the use of synthetic aperture radar principle. A tumor phantom is produced and the experimental results are acquired by using sand, screw and phantoms with the use of an experimental setup prototype that is also presented in this study. The simulation results are verified and validated by experimental results. The resultant images generated using the simulated and experimental data show a good agreement with the designed scenarios.

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Microwave imaging of breast cancer tumor inside voxel-based breast phantom using conformal antennas

Cited by 5 publications

References 8 publications

Detection of Depth of the Tumor in Microwave Imaging Using Ground Penetrating Radar Algorithm

Detection of Depth of the Tumor in Microwave Imaging Using Ground Penetrating Radar Algorithm

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

Implementation of the inverse circular radon transform-based imaging approach for breast cancer screening

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