Neural-network-based inverse-scattering technique for online microwave medical imaging

In this paper, the application of radial basis function neural networks (RBFNNs) in inverse-scattering problems related to microwave medical imaging is proposed. The objective of the networks is to estimate the geometric and/or electromagnetic properties of tissues by processing the scattered-field measurements obtained during the illumination of the body by electromagnetic waves. The training of the RBFNNs is based on the orthogonal least-squares algorithm. This approach results in straightforward construction of the network, where both the size and the values of the free parameters of the network are obtained. The proposed methodology is applied to the estimation of the position and the size of proliferated marrow inside the bone of a limb. This application is closely related to the detection and monitoring of leukemia. Different measurement configurations are examined. The performance of the constructed networks in cases of noisy field measurements is also investigated.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  Ioannis T. Rekanos.  On-line inverse scattering of conducting cylinders using radial basis-function neural networks , 2001 .

[3]  Ann Franchois,et al.  Quantitative microwave imaging with a 2.45-GHz planar microwave camera , 1998, IEEE Transactions on Medical Imaging.

[4]  Salvatore Caorsi,et al.  A threshold electromagnetic classification approach for cylinders embedded in a lossy medium by using a neural network technique , 2000 .

[5]  Peter Monk,et al.  The detection and monitoring of leukemia using electromagnetic waves: numerical analysis , 1995 .

[6]  Paolo Gamba,et al.  Electromagnetic detection of dielectric cylinders by a neural network approach , 1999, IEEE Trans. Geosci. Remote. Sens..

[7]  K. Paulsen,et al.  Near-field microwave imaging of biologically-based materials using a monopole transceiver system , 1998 .

[8]  Ioannis T. Rekanos,et al.  Inverse scattering of dielectric cylinders by using radial basis function neural networks , 2001 .

[9]  Jian-Ming Jin,et al.  The Finite Element Method in Electromagnetics , 1993 .

[10]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[11]  Stavros M. Panas,et al.  Microwave imaging using the finite-element method and a sensitivity analysis approach , 1999, IEEE Transactions on Medical Imaging.

[12]  R. Kress,et al.  Inverse Acoustic and Electromagnetic Scattering Theory , 1992 .