Image Reconstruction of Electrical Impedance Tomography Using Fish School Search and Differential Evolution

Valter Augusto De Freitas Barbosa,Rodrigo Luiz Tomio Ogava,Ítalo José Do Nascimento Silva Araú Dias Dias,Allan Rivalles Souza Feitosa,Manoela Paschoal,David Edson Ribeiro,Reiga Ramalho Ribeiro,Wellington Pinheiro Dos Santos,Rafaela Covello Freitas,Ricardo Emmanuel De Souza,Victor Luiz Bezerra Araújo Da Silva,Rodrigo Beltrão Valença,Natália Souza Soares

doi:10.4018/978-1-5225-5134-8.ch012

Valter Augusto De Freitas Barbosa, Rodrigo Luiz Tomio Ogava + Show 11 more

https://doi.org/10.4018/978-1-5225-5134-8.ch012

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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 and bioinspired computation have become a source of methods for solving inverse problems. In this chapter, the authors investigate the performance of fish school search (FSS) and differential evolution (DE) using non-blind search (NBS) considering meshes of 415, 3190, and 9990 finite elements. The methods were evaluated using numerical phantoms consisting of electrical conductivity images with objects in the center, between the center and the edge, and on the edge of a circular section. Twenty simulations were performed for each configuration. Results showed that both FSS and DE are able to perform EIT image reconstruction with large meshes and converge faster by using non-blind search.

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