This paper presents an image reconstruction method based on parametric level set (PLS) method using electrical impedance tomography. The conductivity to be reconstructed was assumed to be piecewise constant and the geometry of the anomaly was represented by a shape-based PLS function, which we represent using Gaussian radial basis functions (GRBF). The representation of the PLS function significantly reduces the number of unknowns, and circumvents many difficulties that are associated with traditional level set (TLS) methods, such as regularization, re-initialization and use of signed distance function. PLS reconstruction results shown in this article are some of the first ones using experimental EIT data. The performance of the PLS method was tested with water tank data for two-phase visualization and with simulations which demonstrate the most popular biomedical application of EIT: lung imaging. In addition, robustness studies of the PLS method w.r.t width of the Gaussian function and GRBF centers were performed on simulated lung imaging data. The experimental and simulation results show that PLS method has significant improvement in image quality compared with the TLS reconstruction.

This work is concerned with the interfacial boundary estimation in stratified flows of two immiscible liquids using electrical resistance tomography. The interfacial boundary is parametrized with front points and the unknown positions of the front points are estimated based on the relationship between the injected currents and the induced boundary potentials. It is assumed that the interfacial boundary moves during the time taken to collect a full set of independent measurement data. In order to find the unknown interface, the front point locations are treated as state variables, which are tracked through the extended Kalman filter approach. Numerical experiments are successfully conducted for the verification of the proposed approach.

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