scholarly journals Ultra-wide band microwave image reconstruction for early breast cancer detection by norm constrained capon beamforming

2013 ◽  
Vol 58 (1-2) ◽  
pp. 403-408 ◽  
Author(s):  
Li Xu ◽  
Xia Xiao ◽  
Takamaro Kikkawa
Keyword(s):  
Breast Cancer ◽  
Image Reconstruction ◽  
Early Breast Cancer ◽  
Cancer Detection ◽  
Wide Band ◽  
Breast Cancer Detection ◽  
Ultra Wide Band ◽  
Microwave Image

Ultra Wide Band (UWB) Based Early Breast Cancer Detection Using Artificial Intelligence

2020 ◽  
pp. 505-515
Author(s):  
Bifta Sama Bari ◽  
Sabira Khatun ◽  
Kamarul Hawari Ghazali ◽  
Md. Moslemuddin Fakir ◽  
Wan Nur Azhani W. Samsudin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Early Breast Cancer ◽  
Cancer Detection ◽  
Wide Band ◽  
Breast Cancer Detection ◽  
Ultra Wide Band

Breast cancer imaging and detection using microwave technique

2020 ◽  
Author(s):  
◽  
Ahmed Maher Abed
Keyword(s):  
Breast Cancer ◽  
Dielectric Properties ◽  
Cancer Detection ◽  
Wide Band ◽  
Breast Cancer Detection ◽  
Ultra Wide Band ◽  
Late Time ◽  
Simulation Tool ◽  
Esprit Algorithm ◽  
Microwave Breast Cancer Detection

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] Worldwide, breast cancer continues to be the top cause of death among women and the second-leading cause of cancer death after lung cancer. Thus, it has become a great global concern. Years of research on both diagnostic and therapeutic breast cancer detection and imaging using microwave techniques has resulted in a variety of novel approaches and studies. These approaches and studies utilize numerical breast phantoms that model structural complexities, tissue heterogeneity, and dispersive dielectric properties. In this dissertation, a microwave breast cancer detection technique was investigated and Ultra-Wide Band (UWB) radar imaging was used. A UWB antenna was designed and modeled using CST Microwave Studio and was used for Ultra-Wide Band microwave breast cancer detection. A new calibration approach for microwave breast cancer detection was proposed to calibrate the signals before applying the beamforming algorithms. A simulation was also used to validate the proposed techniques. Two signal calibration approaches were proposed to remove the high magnitude clutter from the signals. The two approaches are based on the state-space method Autoregressive Moving Average (ARMA). The first approach is derived from Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT). This algorithm is referred to as the Pole Isolation via ESPRIT algorithm (PI-ESPRIT). The second approach is also derived from a previously proposed algorithm for microwave breast cancer detection. Th second calibration algorithm is referred to as the Modified Pole Removal algorithm. The Modified Pole Removal algorithm works to detect tumors, in contrast to the previously proposed algorithm (Pole Removal) that shows a lack of tumor detection. Three beamforming techniques were used to focus the signals onto the voxels through the breast phantom. Another beamforming algorithm was proposed, along with the Transmitting-Receiving Antenna Separation Distance (TRASD), which allows for the reduction of the late time clutter effect and improvement of the Signal to Clutter Ratio (SCR) when using the PI-ESPRIT algorithm. Using CST simulation tool, antennas arrayed around the breast are designed to simulate the transmitting/receiving signals. Numerical phantoms, with complicated structures and dispersive dielectric properties of breast tissue, have been developed with the CST simulation tool for simulating electromagnetic propagation. The received signals are imported into the MATLAB program to investigate the proposed approaches and compare them to conventional approaches. Overall, the results have shown improvement.

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