Improved Tactile Stimulus Reconstruction in Electrical Impedance Tomography Using the Discrete Cosine Transform and Machine Learning

Electrical impedance tomography (EIT) is a noninvasive imaging method with promising applications in skin-like tactile sensors. Image reconstruction in EIT is challenging due to its nonlinear and ill-posed nature, resulting in fuzzy edges, ringing, and boundary artifacts. These distortions reduce th...

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Bibliographic Details
Published in:IEEE sensors journal 2024-07, Vol.24 (14), p.22084-22095
Main Authors: Husain, Zainab, Liatsis, Panos
Format: Article
Language:English
Subjects:
Discrete cosine transform
Discrete cosine transform (DCT)
Discrete cosine transforms
Electrical impedance
Electrical impedance tomography
electrical impedance tomography (EIT)
Energy distribution
Image coding
Image reconstruction
Imaging
Impedance
Inverse problems
Machine learning
sparsity
State-of-the-art reviews
tactile sensing
Tactile sensors (robotics)
Tomography
Touch
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Summary:Electrical impedance tomography (EIT) is a noninvasive imaging method with promising applications in skin-like tactile sensors. Image reconstruction in EIT is challenging due to its nonlinear and ill-posed nature, resulting in fuzzy edges, ringing, and boundary artifacts. These distortions reduce the accuracy of localizing and discriminating between touch stimuli. This work focuses on EIT difference imaging and proposes a novel representation scheme for changes in the conductivity distribution using the discrete cosine transform (DCT). The superior energy compaction and decorrelation properties of DCT are utilized to efficiently represent the conductivity distribution, which is decoded by a committee of local experts, machine-learning (ML)-based, inverse solvers. The performance of the method is evaluated based on prediction accuracy and reconstruction quality using the mean squared error and structural similarity index (SSIM). The DCT framework outperforms state-of-the-art works and improves SSIM up to 0.67, which is a 21.6% improvement from benchmarked methods and leads to better localization and discrimination of multiple objects.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3338246