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Microwave Tomography pp 87–141Cite as

Inclusion of A Priori Information Using Neural Networks

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Abstract

Microwave image reconstruction is an ill-posed problem. Regularization methods are used to remove the ill-posed answers. However, the regularization methods are often problem independent and have smoothing effects. In this chapter, a novel problem-dependent regularization approach is introduced for the application of breast imaging that exploits a priori information for regularization. A real genetic algorithm (RGA) minimzes a cost functional that is essentially the error between the recorded and simulated data. At each iteration of the RGA, a neural network classifier rejects the solutions that cannot be a map of the dielectric properties of a breast. Although the application presented in this chapter is specific to breast cancer, the idea of using a priori information along with the classification techniques can be generally applied to the scenarios where information about the dielectric properties of the medium exists.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of North Dakota, Grand Forks, ND, USA

    Sima Noghanian

  2. Department of Electrical Engineering, Wilkes University, Wilkes-Barre, PA, USA

    Abas Sabouni

  3. Department of Computer Science, University of North Dakota, Grand Forks, ND, USA

    Travis Desell

  4. Invenia Technical Computing, Winnipeg, MB, Canada

    Ali Ashtari

Authors
  1. Sima Noghanian
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  2. Abas Sabouni
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  3. Travis Desell
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  4. Ali Ashtari
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Noghanian, S., Sabouni, A., Desell, T., Ashtari, A. (2014). Inclusion of A Priori Information Using Neural Networks. In: Microwave Tomography. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0752-6_5

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  • DOI: https://doi.org/10.1007/978-1-4939-0752-6_5

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