Journal of KIISE:Computing Practices and Letters (한국정보과학회논문지:컴퓨팅의 실제 및 레터)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

A Distributed Electrical Impedance Tomography Algorithm for Real-Time Image Reconstruction

실시간 영상 복원을 위한 분산 전기단층촬영 알고리즘

  • Published : 2004.02.01

Abstract

This paper proposes and measures the performance of a distributed EIT (Electrical Impedance Tomography) image reconstruction algorithm which has a master-slave structure. The image construction is a computation based application of which the execute time is proportional to the cube of the unknowns. After receiving a specific frame from the master, each computing node extracts the basic elements by executing the first iteration of Kalman Filter in parallel. Then the master merges the basic element lists into one group and then performs the sequential iterations with the reduced number of unknowns. Every computing node has MATLAB functions as well as extended library implemented for the exchange of MATLAB data structure. The master implements another libraries such as threaded multiplication, partitioned inverse, and fast Jacobian to improve the speed of the serial execution part. The parallel library reduces the reconstruction time of image visualization about by half, while the distributed grouping scheme further reduces by about 12 times for the given target object when there are 4 computing nodes.

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References

  1. M. Cheney, et. al., 'Electrical impedance tomography,' SIAM Review, No. 41, pp.85-101, 1999 https://doi.org/10.1137/S0036144598333613
  2. M. Kim, S. Kim, K. Kim, J. Lee, Y. Lee, Reconstruction of particle concentration distribution on annular Coutte flow using electrical impedance tomography, Journal of Industrial Engineering Chemistry, Oct. 2001
  3. R. Buyya, High Performance Cluster Computing, Vol.2, PTR PH, 1999
  4. W. Feng, J. Liu, 'An extended imprecise computation model for time-constrained speech processing and generation,' IEEE Workshop on Real-Time Applications, pp.76-80, May 1993 https://doi.org/10.1109/RTA.1993.263112
  5. M. Vauhkonen, et. al., 'A MATLAB Package for the EIDORS project to reconstruct two-dimensional EIT images', Physiology Measurement, vol. 22, pp.107-111, 2001 https://doi.org/10.1088/0967-3334/22/1/314
  6. R. Johnson, S. Jayaram, L. Sun, J. Zalewski, 'Distributed Processing Kalman Filter for Automated Vehicle Parameter Estimation: A Case Study,' IASTED Int'l Conference on Control and Applications, Banff, Alberta, Canada, July 24-26, 2000
  7. Woo EJ, Hua P, Webster JG, Tomkins WJ, Pallas-Aren, 'A robust image reconstruction algorithm and its parallel implimentation in electrical impedance tomography,' IEEE Trans. on Medical Imaging, 12(2), pp.137-146, 1993 https://doi.org/10.1109/42.232242
  8. http://www.mathworks.com
  9. S. Pawletta, and et. al., 'A MATLAB toolbox for distributed and parallel processing,' Proc. MATLAB Conference, 1995
  10. K. Gallivan, et. al., Parallel Algorithms for Matrix Computations, SIAM, 1990
  11. E. Horowitz, S. Sahni, S. Anderson, Fundamentals of Data Structures in C, Computer Science Press, 1995
  12. B. Quinn, D. Schute, Windows Sockets Network Programming, Addison-Wesley, 1995
  13. M. Vauhkonen, et. al, 'A Kalman filter approach to track fast impedance changes in electrical impedance tomography,' IEEE Trans. on Biomedical Engineering, pp.486-493, 1998 https://doi.org/10.1109/10.664204
  14. K. H. Cho, S. Kim, Y. J. Lee., 'Impedance imaging of two-phase flow field with mesh grouping algorithm,' Nuclear Eng. & Design, pp.18-26, 2001
  15. P. Pacheco, Parallel Programming with MPI, Morgan-Kaufmann Publishers, 1997