Abstract
This paper proposes an adaptive image enhancement method for electrical impedance tomography (EIT). The images are enhanced based on a steerable and multi-scale resolution enhancement algorithm. It is initiated by capturing the spatial variations in decomposition orientations, and decomposition scales of the EIT image. The interpretation of projected image sub-bands is translated into resolution through statistical processes. A steerable filter containing Gaussian basis function derivatives captures the statistical information. Using the regional quantization method (RQM) proposed in this paper, projection weights are computed through spatial statistics of the image sub-bands and tuned adaptively. RQM assigns more resolution to those directional edges which have higher standard deviation and embeds high-order curvatures into the EIT images while suppressing noise. Comparison with conventional image enhancement methods demonstrates the superior performance of RQM. Using RQM it is shown that for 16, 32 and 64 electrode configurations with noise-free recording of 32 × 32 EIT images the number of electrodes can be reduced by 5, 7 and 12 respectively without loss of detail.
Highlights
Electrical impedance tomography (EIT) provides a continuous, real-time and non-invasive imaging technique for measuring the internal impedance fluctuations of a body from a series of surface electrodes placed on it [1]
The regional quantization method (RQM) processing time increases almost exponentially with respect to the size of the image. It has a longer processing time compared to the other enhancement algorithms,which is as a result of the number of rotational decompositions and identifying image statistics required to spatially enhance the EIT image resolution
The adaptive image enhancement proposed in this paper using RQM is a valuable tool for improving resolution of EIT images
Summary
Electrical impedance tomography (EIT) provides a continuous, real-time and non-invasive imaging technique for measuring the internal impedance fluctuations of a body from a series of surface electrodes placed on it [1]. A third metric, ElectrodesOpt , is used to identify the number of redundant electrodes in EIT systems by considering the signal-to-noise (SNR) of the enhanced and original EIT images. This is followed by two different tests for resolution enhancement investigation, namely the CII of simulated phantoms and identification of the optimized number of electrodes (ElectrodesOpt ).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
