Clustering-Based Reconstruction Algorithm for Electrical Impedance Tomography

Electrical impedance tomography (EIT) is an advanced visualization technique characterized by its cost-effectiveness, rapid response, non-ionizing radiation, and non-intrusiveness compared with other tomography modalities. However, the quality of EIT imaging is compromised by the inherent ill-posed solution and the soft-field effect, which hinder the effective reconstruction of target objects. Various methods have been employed to address these issues, but they can make limited progress in high-quality reconstruction. In this article, efforts are made by applying the clustering technique to the EIT reconstruction. The key features hidden in all EIT measurements are extracted, and whereby each pixel can be represented by a feature vector. After clustering these vectors over all pixels, we proposed a novel EIT reconstruction algorithm. Experimental results show that the novel algorithm can reconstruct the EIT images more accurately and effectively than the existing algorithms. Consequently, the implementation of a clustering algorithm instead of the existing algorithm offers a practical and effective approach to enhance the EIT imaging quality.