Reconstruction of Electrical Impedance Tomography Using Fish School Search, Non-Blind Search, and Genetic Algorithm

TL;DR

This paper introduces a novel EIT image reconstruction method combining Fish School Search and Non-Blind Search, demonstrating faster convergence than genetic algorithms through numerical simulations.

Contribution

The paper presents a new EIT reconstruction approach using Fish School Search and Non-Blind Search, outperforming genetic algorithms in convergence speed.

Findings

FSS and FSS-NBS converge faster than genetic algorithms.

The method effectively reconstructs conductivity images in various configurations.

Numerical simulations validate the efficiency of the proposed approach.

Abstract

Electrical Impedance Tomography (EIT) is a noninvasive imaging technique that does not use ionizing radiation, with application both in environmental sciences and in health. Image reconstruction is performed by solving an inverse problem and ill-posed. Evolutionary Computation and Swarm Intelligence have become a source of methods for solving inverse problems. Fish School Search (FSS) is a promising search and optimization method, based on the dynamics of schools of fish. In this article the authors present a method for reconstruction of EIT images based on FSS and Non-Blind Search (NBS). The method was evaluated using numerical phantoms consisting of electrical conductivity images with subjects in the center, between the center and the edge and on the edge of a circular section, with meshes of 415 finite elements. The authors performed 20 simulations for each configuration. Results showed that both FSS and FSS-NBS were able to converge faster than genetic algorithms.