This study addresses the numerical treatment applied to the singular values of the sensitivity matrix in the presence of noisy measurements, subsequently suggesting electrode configurations that provide sensitivities with improved characteristics. We begin by examining the impact of the individual singular values on the spatial resolution of the image and then proceed to express the generalized Tikhonov regularization in terms of the generalized singular value decomposition in order to demonstrate how the reconstructed image is synthesized from the individual energy components. The electrode segmentation scheme is then introduced as a feasible configuration offering efficient and improved resolution impedance imaging. Finally, the regularized total least squares algorithm is implemented to provide the linear step solution within Newton's iterative scheme. Images of several reconstructed inhomogeneities are presented, using simulated measurements obtained from the segmented electrodes system.