Date of Award

August 2021

Document Type


Degree Name

Master of Science (MS)


Mathematical Sciences

Committee Member

Taufiquar Khan

Committee Member

Shitao Liu

Committee Member

Andrew Brown


Electrical impedance tomography (EIT) has many significant applications and has gained popularity due to its ease of use and its non-invasiveness. While it has notable applications, EIT is severely ill-posed. Since EIT is ill-posed, various techniques have been considered to solve the inverse problem. While the inverse problem has been well-studied, a common question is to ask how we can improve in solving the inverse problem of the electrical impedance tomography?

In this thesis, we consider using image segmentation alongside with iteratively-regularized Gauss-Newton Method (IRGN) to solve the inverse problem of EIT in various geometries. A comparison between the reconstruction of the image with IRGN and with IRGN alongside image segmentation is presented. Alongside the comparison, we analyze the parameter and residual error between IRGN and image segmentation to show the efficiency of image segmentation in solving the inverse problem of EIT. In the end, we discuss future work that could be done to extend the results of this thesis.



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