Image reconstruction in EIT with unreliable electrode data using random sample consensus method

In electrical impedance tomography (EIT), it is important to acquire reliable measurement data through EIT system for achieving good reconstructed image. In order to have reliable data, various methods for checking and optimizing the EIT measurement system have been studied. However, most of the methods involve additional cost for testing and the measurement setup is often evaluated before the experiment. It is useful to have a method which can detect the faulty electrode data during the experiment without any additional cost. This paper presents a method based on random sample consensus (RANSAC) to find the incorrect data on fault electrode in EIT data. RANSAC is a curve fitting method that removes the outlier data from measurement data. RANSAC method is applied with Gauss Newton (GN) method for image reconstruction of human thorax with faulty data. Numerical and phantom experiments are performed and the reconstruction performance of the proposed RANSAC method with GN is compared with conventional GN method. From the results, it can be noticed that RANSAC with GN has better reconstruction performance than conventional GN method with faulty electrode data.