Breast tumour identification based on inverse scattering approach. Issue 7 (1st May 2013)

• Record Type: Journal Article
• Authors: Firoozy, Nariman; Tavakoli, Ahad
• Is Part Of: IET microwaves, antennas & propagation. Volume 7:Issue 7(2013)
• Page Start: 535
• Page End: 542
• Publication Date: 2013-05-01
• Subjects: biomedical measurement -- directive antennas -- electromagnetic wave scattering -- evolutionary computation -- finite difference time‐domain analysis -- iterative methods -- particle swarm optimisation -- tumours; breast tumour size identification -- inverse scattering approach -- iterative evolutionary method -- breast tumour detection -- breast tumour location identification -- wide‐band Gaussian pulses -- chirping technique -- directional antenna -- three‐dimensional scan -- finite‐difference time‐domain technique -- incident wave -- breast electromagnetic model -- tumour specification -- cost function -- particle swarm optimisation -- differential evolution -- TE mode -- TM mode; Microwaves -- Periodicals; Microwave antennas -- Periodicals; Antennas (Electronics) -- Periodicals; Radio wave propagation -- Periodicals; Microwave communication systems -- Periodicals; 621.381305
• DOI: 10.1049/iet-map.2012.0618
• Languages: English
• ISSNs: 1751-8725

Abstract

Abstract : This study introduces an iterative evolutionary method in detection of breast tumour, identification of its size and location. At primary measurement step, a set of antennas capable of transmitting wide‐band Gaussian pulses, generated through the chirping technique, is placed around the breast and total wave received by each directional antenna is recorded in a bistatic manner for every cross section of the breast corresponding to a specific height for a full three‐dimensional scan. Each set of data for each cross section is then analysed separately. Measured fields are used in an inverse‐scattering problem, where the unknown is the tumour. To solve this problem, the finite‐difference time‐domain technique produces the total field resulted from the same incident wave as in measurement while illuminating an electromagnetic model of the breast, each time having a new tumour specification as a part of an evolutionary algorithm scheme. A comparison between various generated and measured reference data is repeated to reach a minimum cost function that corresponds to a minimum error in identification. Particle swarm optimisation and differential evolution are the two optimisations, each using all field elements of both TE z and TM z modes to reach convergence. The final results show an accurate identification of the tumour by this method.