Impact of the Radial Basis Function Spread Factor onto Image Reconstruction in Electrical Impedance Tomography

Abstract The major problem of the Electrical impedance tomography (EIT) is to get the resistivity distribution image of a given cross-sectional area. There are many methods solving this non-linear problem, mostly requiring certain simplifications and assumptions. Most of the methods are also computationally demanding and not easy to implement. The usage of the neural networks appears to be a solution of the mentioned problems. In this article we continued with our previous study and used Radial basis function (RBF) neural network for image reconstruction in electrical impedance tomography and we focused on examining how the change of the spread parameter of the RBF influences the result of the image reconstruction with the RBF neural network.

[1]  Chao Wang,et al.  RBF neural network image reconstruction for electrical impedance tomography , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[2]  Michal Prauzek,et al.  Image reconstruction in electrical impedance tomography using neural network , 2014, 2014 Cairo International Biomedical Engineering Conference (CIBEC).

[3]  Lili Xie,et al.  Application of PSO algorithm and RBF neural network in electrical impedance tomography , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[4]  Andy Adler,et al.  A neural network image reconstruction technique for electrical impedance tomography , 1994, IEEE Trans. Medical Imaging.

[5]  Michal Prauzek,et al.  A hybrid device for electrical impedance tomography and bioelectrical impedance spectroscopy measurement , 2014, 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE).

[6]  Silvia Conforto,et al.  Reconstruction of ECG Precordial Leads by PCA and Neural Networks , 2013 .

[7]  H.-C. Kim,et al.  Electrical impedance tomography reconstruction algorithm using extended Kalman filter , 2001, ISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No.01TH8570).

[8]  Juan Zhang,et al.  An ECT System Based on Improved RBF Network and Adaptive Wavelet Image Enhancement for Solid/Gas Two-phase Flow , 2012 .

[9]  Peng Wang,et al.  The Implementation of FEM and RBF Neural Network in EIT , 2009, 2009 Second International Conference on Intelligent Networks and Intelligent Systems.

[10]  Feng Fu,et al.  Comparative Study of Reconstruction Algorithms for Electrical Impedance Tomography , 2012, 2012 Spring Congress on Engineering and Technology.

[11]  Yongjian Li,et al.  A Numerical Computation Forward Problem Model of Electrical Impedance Tomography Based on Generalized Finite Element Method , 2014, IEEE Transactions on Magnetics.

[12]  William R B Lionheart,et al.  Uses and abuses of EIDORS: an extensible software base for EIT , 2006, Physiological measurement.

[13]  Guizhi Xu,et al.  A reconstruction algorithm based on wavelet network in electrical impedance tomography , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.