Level set reconstruction algorithm for 3D ECT

There is growing interest in using new and emerging imaging techniques for industrial process monitoring; particularly 3D electrical capacitance tomography (ECT) is gaining popularity in past couple of years. In this paper we present a level set method applied to 3D ECT imaging using experimental data. The finite element models were developed based on a 32 electrodes ECT system. Development of the level set technique will enable detection of smaller objects and improve the accuracy of inclusion boundary shape. We propose a shape based method rather than traditional image based methods. The shape-based approach offers several advantages compared to more traditional voxel-based approaches, for example well-defined boundaries. The incorporation of an intrinsic regularization in the form of a-priori assumptions regarding the general anatomical structures present in the medium reduces the dimensionality of the inverse problem and thereby stabilizes the reconstruction. The level set strategy (which is an implicit representation of the shapes) frees us from topological restrictions during this reconstruction process.