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Using Extreme Learning Machines and the Backprojection Algorithm as an Alternative to Reconstruct Electrical Impedance Tomography Images
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Author(s): Juliana Carneiro Gomes (Escola Politécnica, Universidade de Pernambuco, Brazil), Maíra Araújo de Santana (Universidade Federal de Pernambuco, Brazil), Clarisse Lins de Lima (Universidade Federal de Pernambuco, Brazil), Ricardo Emmanuel de Souza (Universidade Federal de Pernambuco, Brazil)and Wellington Pinheiro dos Santos (Universidade Federal de Pernambuco, Brazil)
Copyright: 2021
Pages: 12
Source title:
Biomedical Computing for Breast Cancer Detection and Diagnosis
Source Author(s)/Editor(s): Wellington Pinheiro dos Santos (Universidade Federal de Pernambuco, Brazil), Washington Wagner Azevedo da Silva (Universidade Federal de Pernambuco, Brazil)and Maira Araujo de Santana (Universidade Federal de Pernambuco, Brazil)
DOI: 10.4018/978-1-7998-3456-4.ch002
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Abstract
Electrical Impedance Tomography (EIT) is an imaging technique based on the excitation of electrode pairs applied to the surface of the imaged region. The electrical potentials generated from alternating current excitation are measured and then applied to boundary-based reconstruction methods. When compared to other imaging techniques, EIT is considered a low-cost technique without ionizing radiation emission, safer for patients. However, the resolution is still low, depending on efficient reconstruction methods and low computational cost. EIT has the potential to be used as an alternative test for early detection of breast lesions in general. The most accurate reconstruction methods tend to be very costly as they use optimization methods as a support. Backprojection tends to be rapid but more inaccurate. In this work, the authors propose a hybrid method, based on extreme learning machines and backprojection for EIT reconstruction. The results were applied to numerical phantoms and were considered adequate, with potential to be improved using post processing techniques.
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