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

2018 ◽  
pp. 301-338 ◽  
Author(s):  
Valter Augusto de Freitas Barbosa ◽  
Wellington Pinheiro dos Santos ◽  
Ricardo Emmanuel de Souza ◽  
Reiga Ramalho Ribeiro ◽  
Allan Rivalles Souza Feitosa ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Differential Evolution ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Impedance Tomography ◽  
Circular Section ◽  
Fish School ◽  
Noninvasive Imaging Technique ◽  
Ill Posed ◽  
Blind Search

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.

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

2017 ◽  
Vol 8 (2) ◽  
pp. 17-33 ◽  
Author(s):  
Valter A. F. Barbosa ◽  
Reiga R. Ribeiro ◽  
Allan R. S. Feitosa ◽  
Victor L. B. A. Silva ◽  
Arthur D. D. Rocha ◽  
...  
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Imaging Technique ◽  
Optimization Method ◽  
Impedance Tomography ◽  
Circular Section ◽  
Fish School ◽  
Noninvasive Imaging Technique ◽  
Ill Posed ◽  
Blind Search

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.

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

Biotechnology ◽  
2019 ◽  
pp. 2021-2038 ◽  
Author(s):  
Valter A. F. Barbosa ◽  
Reiga R. Ribeiro ◽  
Allan R. S. Feitosa ◽  
Victor L. B. A. Silva ◽  
Arthur D. D. Rocha ◽  
...  
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Optimization Method ◽  
Environmental Sciences ◽  
Impedance Tomography ◽  
Circular Section ◽  
Fish School ◽  
Noninvasive Imaging Technique ◽  
Ill Posed ◽  
Blind Search

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.

Patch-based sparse reconstruction for electrical impedance tomography

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 257-269 ◽  
Author(s):  
Qi Wang ◽  
Pengcheng Zhang ◽  
Jianming Wang ◽  
Qingliang Chen ◽  
Zhijie Lian ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Sparse Representation ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Inverse Operator ◽  
Reconstruction Algorithm ◽  
Impedance Tomography ◽  
Content Type ◽  
Ill Posed ◽  
High Level

Purpose Electrical impedance tomography (EIT) is a technique for reconstructing the conductivity distribution by injecting currents at the boundary of a subject and measuring the resulting changes in voltage. Image reconstruction for EIT is a nonlinear problem. A generalized inverse operator is usually ill-posed and ill-conditioned. Therefore, the solutions for EIT are not unique and highly sensitive to the measurement noise. Design/methodology/approach This paper develops a novel image reconstruction algorithm for EIT based on patch-based sparse representation. The sparsifying dictionary optimization and image reconstruction are performed alternately. Two patch-based sparsity, namely, square-patch sparsity and column-patch sparsity, are discussed and compared with the global sparsity. Findings Both simulation and experimental results indicate that the patch based sparsity method can improve the quality of image reconstruction and tolerate a relatively high level of noise in the measured voltages. Originality/value EIT image is reconstructed based on patch-based sparse representation. Square-patch sparsity and column-patch sparsity are proposed and compared. Sparse dictionary optimization and image reconstruction are performed alternately. The new method tolerates a relatively high level of noise in measured voltages.

scholarly journals Image Reconstruction Based on Homotopy Perturbation Inversion Method for Electrical Impedance Tomography

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jing Wang ◽  
Bo Han
Keyword(s):  
Image Reconstruction ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Perturbation Technique ◽  
Inversion Method ◽  
Impedance Tomography ◽  
Data Noise ◽  
Homotopy Perturbation ◽  
Ill Posed ◽  
Homotopy Perturbation Technique

The image reconstruction for electrical impedance tomography (EIT) mathematically is a typed nonlinear ill-posed inverse problem. In this paper, a novel iteration regularization scheme based on the homotopy perturbation technique, namely, homotopy perturbation inversion method, is applied to investigate the EIT image reconstruction problem. To verify the feasibility and effectiveness, simulations of image reconstruction have been performed in terms of considering different locations, sizes, and numbers of the inclusions, as well as robustness to data noise. Numerical results indicate that this method can overcome the numerical instability and is robust to data noise in the EIT image reconstruction. Moreover, compared with the classical Landweber iteration method, our approach improves the convergence rate. The results are promising.

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