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

Valter A. F. Barbosa, Reiga R. Ribeiro, Allan R. S. Feitosa, Victor L. B. A. Silva, Arthur D. D. Rocha, Rafaela C. Freitas, Ricardo E. Souza and Wellington P. Santos
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Valter A. F. Barbosa: Departamento de Engenharia Biomédica, Universidade Federal de Pernambuco, Recife, Brazil
Reiga R. Ribeiro: Departamento de Engenharia Biomédica, Universidade Federal de Pernambuco, Recife, Brazil
Allan R. S. Feitosa: Departamento de Engenharia Biomédica, Universidade Federal de Pernambuco, Recife, Brazil
Victor L. B. A. Silva: Escola Politécnica da Universidade de Pernambuco, POLI-UPE, Recife, Brazil
Arthur D. D. Rocha: Escola Politécnica da Universidade de Pernambuco, POLI-UPE, Recife, Brazil
Rafaela C. Freitas: Escola Politécnica da Universidade de Pernambuco, POLI-UPE, Madalena, Recife, Brazil
Ricardo E. Souza: Departamento de Engenharia Biomédica, Universidade Federal de Pernambuco, Recife, Brazil
Wellington P. Santos: Departamento de Engenharia Biomédica, Universidade Federal de Pernambuco, Recife, Brazil & Escola Politécnica da Universidade de Pernambuco, POLI-UPE, Recife, Brazil

International Journal of Swarm Intelligence Research (IJSIR), 2017, vol. 8, issue 2, 17-33

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jsir00:v:8:y:2017:i:2:p:17-33

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