Improving Conductivity Image Quality Using Block Matrix-based Multiple Regularization (BMMR) Technique in EIT: A Simulation Study

Tushar Kanti Bera, Samir Kumar Biswas, K. Rajan, J. Nagaraju


A Block Matrix based Multiple Regularization (BMMR) technique is proposed for improving conductivity image quality in EIT. The response matrix (JTJ) has been partitioned into several sub-block matrices and the highest eigenvalue of each sub-block matrices has been chosen as regularization parameter for the nodes contained by that sub-block. Simulated boundary data are generated for circular domain with circular inhomogeneity and the conductivity images are reconstructed in a Model Based Iterative Image Reconstruction (MoBIIR) algorithm. Conductivity images are reconstructed with BMMR technique and the results are compared with the Single-step Tikhonov Regularization (STR) and modified Levenberg-Marquardt Regularization (LMR) methods. It is observed that the BMMR technique reduces the projection error and solution error and improves the conductivity reconstruction in EIT. Result show that the BMMR method also improves the image contrast and inhomogeneity conductivity profile and hence the reconstructed image quality is enhanced. ;

J Electr Bioimp, vol. 2, pp. 33-47, 2011


EIT, MoBIIR, Jacobian, Block Matrix-based Multiple Regularization (BMMR), simulated boundary data, conductivity imaging, STR, LMR, normalized projection error, normalized solution error.

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