Use of Self-Organizing Kohonen Maps for Quantization of Tomography Images

In this work, a proof of concept is presented, which shows that EIT images reconstructed by EIDORS using methods based on the Finite Element Method can be transformed into less smooth versions using a combination of unsupervised classification and quantization. For that, Kohonen's Self-Organizing Maps were used. The objective is to group the image areas into well-defined regions by reducing the smoothing of the reconstructed image, thus allowing readings to be performed that are more strongly associated with the regions of interest. EIDORS is a reference tool that forms a comparative basis for research developments on the subject of EIT. The generation of this article is part of broader studies in the field of optimization of EIT tomographic images. Studies in the area of image reconstruction using Compressed Sensing combined with Computational Intelligence techniques stand out, with the aim of reducing computational cost, increasing processing speed and increasing the quality of the reconstructed image.