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A neural network approach to microwave imaging
Author(s) -
Wang Yuanmei,
Gong Xing
Publication year - 2000
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/1098-1098(2000)11:3<159::aid-ima1000>3.0.co;2-o
Subject(s) - microwave imaging , artificial neural network , computer science , algorithm , inverse problem , a priori and a posteriori , markov random field , inverse scattering problem , point process , microwave , scattering , image (mathematics) , artificial intelligence , optics , physics , mathematics , mathematical analysis , telecommunications , image segmentation , philosophy , statistics , epistemology
We present a neural network approach to microwave imaging for medical diagnosis. The problem is to reconstruct the complex permittivity of the biological tissues illuminated by the transverse magnetic (TM) incident waves. In order to avoid the inherent ill‐posedness of the inverse scattering problem, we introduce a stochastic process based on Markov random field and a priori knowledge. A coupled gradient neural network is proposed to deal with the mixed‐variable problem because the reconstructed dielectric permittivities are continuous complex variables and the line processes, which can preserve the edges of the reconstructed image, are binary variables. We report the numerical results of a simple human forearm model. We also point out the advantages and the limitations of this method. © 2001 John Wiley & Sons, Inc. Int J Imaging Syst Technol 11, 159–163, 2000