Modified 16-Quasi Log Periodic Antenna Array for Microwave Imaging of Breast Cancer Detection

In this paper, an effective system for microwave imaging of breast tumor detection using modified 16-planar log periodic antenna (PLPA) array is presented. The modified PLPA operates in the band from 2 to 5 GHz with stable directional patterns in the end-fire direction. Once the results of a single antenna element have been validated, the design is extended to include 16 antenna elements. All 16 transceiver antennas are vertically placed around the phantom in a circular manner where one antenna acts as a transmitter and the rest work as receivers. Delay and Sum (DAS) algorithm is used for post processing the acquired scattered signals from the sensors to reconstruct the image of the breast and to identify the existence of breast tumors. The electromagnetic simulators CST and HFSS are used to design the system, while MATLAB is used to process the data. The developed PLPA array-based microwave imaging system performs admirably, making it one of the most effective systems for detecting tumor cells.

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Textile Monopole Sensors for Breast Cancer Detection

10.21203/rs.3.rs-1074337/v1 ◽

2021 ◽

Author(s):

Dalia Mohamed N M K Elsheakh ◽

Soha A. Alsherif ◽

Angie R. Eldamak

Keyword(s):

Breast Cancer ◽

Cancer Detection ◽

Imaging System ◽

Imaging Techniques ◽

Microwave Imaging ◽

Breast Cancer Detection ◽

Ultra Wideband ◽

Impedance Bandwidth ◽

Antenna Element ◽

Textile Sensor

Abstract This paper investigate different available breast cancer imaging methods, particularly microwave imaging techniques (MI). The building block of a radar-based microwave imaging system using a flexible antenna element that could be integrated in a clothing item. It could be accessible to women everywhere easily and at an affordable price which will help them with early breast cancer detection. Two different flexible monopole antennas on a cotton substrate are designed for radar-based microwave imaging. The ultra-wideband (UWB) fully textile sensor shaped as rectangular and circular monopole antenna for breast cancer detection (BCD) are designed. The antenna operates at impedance bandwidth \(\le\)-10dB in the operating band extend from 2.5 to 9 GHz with an overall footprint of 50 × 50 mm2. Simulated detection and bending capacity then proceeded to fabricate a breast phantom and a tumor sample with parameters that mimic these of the human breast’s healthy and malignant tissue. Measurements highly match with the simulation results as well as the performance of antenna before and after subjected to washing is measured and compared. Moreover, simulations of antenna in proximity to breast model with and without tumor are also conducted. Finally the specific absorption rate (SAR) is also calculated to insure that the developed textile sensor is safe to be deployed on-body. The proposed work demonstrates the potential to develop wearable microwave imaging system using fully textile antennas.

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UWB microwave imaging system with a novel calibration approach for breast cancer detection

2011 IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems ◽

10.1109/biowireless.2011.5724350 ◽

2011 ◽

Cited By ~ 5

Author(s):

Yazhou Wang ◽

Aly E. Fathy ◽

Mohamed R. Mahfouz

Keyword(s):

Breast Cancer ◽

Cancer Detection ◽

Imaging System ◽

Microwave Imaging ◽

Breast Cancer Detection ◽

Calibration Approach

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ROTATING ANTENNA MICROWAVE IMAGING SYSTEM FOR BREAST CANCER DETECTION

Progress In Electromagnetics Research ◽

10.2528/pier10071002 ◽

2010 ◽

Vol 107 ◽

pp. 203-217 ◽

Cited By ~ 19

Author(s):

Martin O'Halloran ◽

Martin Glavin ◽

Edward Jones

Keyword(s):

Breast Cancer ◽

Cancer Detection ◽

Imaging System ◽

Microwave Imaging ◽

Breast Cancer Detection

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Holographic Microwave Imaging for Breast Cancer Detection Based on Compressive Sensing

Volume 3: Biomedical and Biotechnology Engineering ◽

10.1115/imece2017-70063 ◽

2017 ◽

Author(s):

Lulu Wang ◽

Hu Peng

Keyword(s):

Breast Cancer ◽

Compressive Sensing ◽

Cancer Detection ◽

Imaging System ◽

Measurement Data ◽

Operation Time ◽

Microwave Imaging ◽

Breast Cancer Detection ◽

Split Bregman ◽

Small Breast

Microwave imaging (MI) has been considered as an alternative way to X-ray mammography for breast cancer detection. This paper presents a compressive sensing based holographic microwave imaging (CS-HMI) approach for diagnosing of breast cancer. A numerical imaging system is developed to validate the proposed CS-HMI approach, which includes a realistic human breast phantom and measurement model. Small breast tumour can be detected in the reconstructed CS-HMI image via Split Bregman (SB) with using 10% measurement data. Simulation and experimental results show that CS-HMI has the ability to produce high quality image by using significantly less measurement data and operation time.

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Breast Tumor Detection Via Fuzzy Morphological Operations

International Journal of Advanced Pervasive and Ubiquitous Computing ◽

10.4018/ijapuc.2019010103 ◽

2019 ◽

Vol 11 (1) ◽

pp. 33-44

Author(s):

Mohammed Y. Kamil ◽

Ali Mohammed Salih

Keyword(s):

Breast Cancer ◽

Cancer Detection ◽

Breast Tumor ◽

Early Detection Of Cancer ◽

Breast Tumors ◽

Morphological Operations ◽

Key Factors ◽

Fuzzy Morphology ◽

High Flexibility ◽

Statistical Results

Breast cancer is one of most dangerous diseases and more common in women. The early detection of cancer is one of the most key factors for possible cure. There are numerous methods of diagnosis amongst which: clinical examination, sonar and mammography, which is the best and more effective in detecting breast cancer. Detection of breast tumors is difficult because of the weak illumination in the image and the overlap between regions. Segmentation is one the crucial steps in locating the tumors, which is an important method of diagnosis of the computer. In this study, segmentation techniques are proposed based on; classic morphology and fuzzy morphology, and a comparison between them. The proposed methods were tested using the database of mini -MIAS, which contains 322 images. After the comparison the statistical results, it shows, the detection of tumor boundary with fuzzy morphology give the higher accuracy than the results in classic morphology. The accuracy is 60.69%, 58.61% respectively due to the high flexibility of foggy logic in dealing with the low lighting in the medical images.

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