Enhancing sharp features by locally relaxing regularization for reconstructed images in electrical impedance tomography

Image reconstruction in EIT is an inverse problem, which is ill posed and hence needs regularization. Regularization brings stability to reconstructed EIT image with respect to noise in the measured data. But this is at the cost of smoothening of sharp edges and high curvature details of shapes in the image, affecting the quality. We propose a novel iterative regularization method based on detection of probable location of the inclusion, for locally relaxing the regularization by appropriate amount, to overcome this problem. Local relaxation around inclusion allows reconstruction of its high curvature shape details or sharp features at the same time giving benefits of higher regularization in remaining areas of the image. The proposed method called DeTER is implemented using a small plug-in to EIDORS (Electrical Impedance and Diffused Optical Reconstruction Software) in a MATLAB environment. Parameters like CNR, correlation coefficients of shape descriptor functions and relative size of reconstructed targets have been computed to evaluate the effectiveness of the technique. The performance of DeTER is tested and verified on simulated data added with Gaussian noise for inclusions of different shapes. Both conducting and nonconducting inclusions are considered. The method is validated using open EIT data shared by ‘Finnish inverse problem society’ and also by reconstructing image of internal void of a papaya fruit from the data acquired by an EIT system developed in our laboratory. The reconstructed images corresponding to the open EIT data clearly show the shapes similar to original objects, with sharp edges and curvature details. The shapes obtained in the papaya image are shown to correspond to the actual void using shape descriptor function. The results demonstrate that the proposed method enhances the sharp features in the reconstructed image with few iterations without causing geometric distortions like smoothening or rounding of the edges.