NDLI: Reconstruction of brain electrical impedance tomography images using particle swarm optimization

Content Provider	IEEE Xplore Digital Library
Author	Pravin Kumar, S. Sriraam, N. Benakop, P.G. Jinaga, B.C.
Copyright Year	2010
Description	Author affiliation: Center for Biomedical Informatics and Signal Processing, SSN College of Engineering, Kalavakkam, TN, India (Pravin Kumar, S.; Sriraam, N.) \|\| Aurora's Technological & Research Institute, Hyderabad, AP, India (Benakop, P.G.) \|\| Jawaharlal Nehru Technological University, Hyderabad, AP, India (Jinaga, B.C.)
Abstract	This paper proposes a particle swarm optimization technique for solving inverse problems to improve the reconstruction of brain electrical impedance tomography (EIT) images. A mesh of 2D head model comprising of 1024 elements is constructed using finite element method (FEM) and the EIT inverse problem is solved by varying the swarm sizes. The performance of the proposed PSO-EIT is evaluated in terms of fidelity measures, such as total relative error and signal to noise ratio (SNR) and the experimental results are compared with other known techniques such as genetic algorithm and modified Newton - Raphson (MNR) method. It can be seen from the experimental results that the proposed PSO-EIT yield significant results in terms of better reconstruction quality compared to other techniques reported in the literature. Thus the PSO-EIT scheme found to be a promising tool for solving reconstruction problem in brain imaging.
Starting Page	339
Ending Page	342
File Size	1701928
Page Count	4
File Format	PDF
ISBN	9781424466511
e-ISBN	9781424466535
DOI	10.1109/ICIINFS.2010.5578681
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-29
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	EIT Tomography Conductivity particle swarm optimization image reconstruction Impedance Finite element methods Image reconstruction Signal to noise ratio
Content Type	Text
Resource Type	Article

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