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Permittivity estimation for breast cancer detection using particle swarm optimization algorithm.

Arezoo Modiri1, Kamran Kiasaleh

  • 1Department of Electrical Engineering, University of Texas at Dallas, TX 75080-3021, USA. arezoo.modiri@student.utdallas.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
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Particle swarm optimization (PSO) effectively estimates tissue permittivities for microwave radiometry (MWR) cancer detection. This method accurately distinguishes tissues, even with limited prior information.

Area of Science:

  • Electromagnetics
  • Biomedical Engineering
  • Computational Intelligence

Background:

  • Microwave radiometry (MWR) shows promise for cancer detection, particularly breast cancer.
  • Accurate tissue permittivity estimation is crucial for MWR data analysis and inverse problem solving.
  • Existing algorithms for MWR inverse problems report varying accuracy levels.

Purpose of the Study:

  • To evaluate the potential of particle swarm optimization (PSO) for estimating tissue permittivities at microwave frequencies (1-2.25 GHz).
  • To develop and compare PSO algorithms for MWR inverse problems under scenarios with and without a priori tissue knowledge.
  • To address limitations of previous PSO studies by incorporating measurement-based loss coefficients relevant to biological tissues.

Main Methods:

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Clinical Imaging of Microwave Mammography
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Last Updated: May 25, 2026

Microfluidic Device for the Separation of Non-Metastatic (MCF-7) and Non-Tumor (MCF-10A) Breast Cancer Cells Using AC Dielectrophoresis
08:33

Microfluidic Device for the Separation of Non-Metastatic (MCF-7) and Non-Tumor (MCF-10A) Breast Cancer Cells Using AC Dielectrophoresis

Published on: August 11, 2022

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

  • Application of particle swarm optimization (PSO) algorithm for permittivity estimation in layered tissues.
  • Development of two distinct PSO algorithms: one assuming no a priori knowledge, the other assuming a priori knowledge.
  • Inclusion of measurement-based loss coefficients to account for signal attenuation in biological tissues.

    • Main Results:

    • The PSO algorithm demonstrated relatively fast convergence.
    • The developed algorithms successfully distinguished between different tissue types with acceptable accuracy.
    • The inclusion of loss coefficients improved the validity of the PSO method for body tissue analysis.

    Conclusions:

    • PSO is a viable and accurate technique for estimating tissue permittivities in microwave radiometry.
    • The developed PSO approaches offer improved solutions for MWR inverse scattering problems, especially for biological tissues.
    • This study highlights the effectiveness of PSO in enhancing cancer detection capabilities using MWR.