Fixed-point Sensitivity Maps for Image Reconstruction in Tomography

Authors

  • Rahman Amirulah Embedded Computing Systems (EmbCoS) Research Focus Group, Computer Engineering Department, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
  • Siti Zarina Mohd Muji Research Focus Group, Computer Engineering Department, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
  • Mohamad Hairol Jabbar Research Focus Group, Computer Engineering Department, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
  • M. Fadzli Abdul Syaib Research Focus Group, Computer Engineering Department, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
  • Ruzairi Abdul Rahim Protom-I Research Group, Infocomm Research Alliance, Control and Mechatronic Engineering Department, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Mohd Nasir Mahmood Language Academy, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/jt.v73.4412

Keywords:

Image reconstruction, optical tomography, fan beam, sensitivity maps

Abstract

This study proposes new fixed-point sensitivity maps for image reconstruction in optical tomography systems by using a linear back projection (LBP) algorithm. The projection selected is based on fan beam orientation with 16 pairs of transmitters and receivers. Many optical tomography systems previously published, focused on microcontroller implementations, which have limited processing speed for real time systems to be used in critical applications such as underwater gas transmission pipeline. To gain benefits from parallel processing in digital hardware implementations such as using FPGA or ASIC, slight modification must be done in the reconstructed images mechanism. The normalized sensitivity maps for image reconstruction are recreated without using floating numbers to optimize the digital design implementations. By multiplying the sensitivity maps matrix with 128, the new sensitivity maps matrices are developed and rounding processes are performed to eliminate floating numbers. The error was determined using Normalized Mean Square Error (NMSE) and the results show that the new fixed-point sensitivity maps produced comparable image reconstruction quality for optical tomography systems with NMSE values of 1.88 × 10-7 and 0.02 for phantoms (a) and (b) respectively.  

References

M. Fadzli, et al. 2012. A Study on Optical Sensors Orientation for Tomography System Development. Sensor & Transducers Journal. 140(5): 45–52.

Ruzairi Abdul Rahim, et al. 2011. Optical Tomography: Real-time Image Reconstruction System using Two Data Processing Units. Jurnal Teknologi. 55(Sains & Kej) Keluaran Khas (2): 63–73.

S. Z. M. Muji, et al. 2013. Optical Tomography Hardware Developmentfor Solid Gas Measurement using Mixed Projection. Flow Measurement ans Instrumentation. 33: 110–121.

Ruzairi Abdul Rahim, et al. 2013. Hardware Design of Laser Optical Tomography System for Detection of Bubbles Column. Jurnal Teknologi (Sciences & Engineering). 64(5): 69–73.

Ruzairi Abdul Rahim et al. 2014. A Review of Process Tomography Application in Inspection System. Jurnal Teknologi (Sciences & Engineering). 70(3): 35–39.

Ruzairi Abdul Rahim et al. 2014. A Review of Optical Tomography System. Jurnal Teknologi (Sciences & Engineering). 69(8): 1–6.

Wei Mingsheng, et al. 2012. Detection of Coal Dust in a Mine Using Optical Tomography. International Journal of Mining Science and Technology. 22: 523–527.

R. A. Williams and M. S. Beck. 1995. Process Tomography: Principles, Techniques, and Applications. Butterworth-Heinemann Ltd. 624.

M. T. M. Khairi, et al. 2012. A Review on Application of Optical Tomography in Industrial Process. International Journal on Smart Sensing and Intelligent Systems. 5(4): 767–798.

Siti Zarina Mohd Muji, et al. 2011. Optical Tomography: A Review on Sensor Array, Projection Arrangement and Image Reconstruction Algorithm. International Journal of Innovative Computing, Information and Control. 7(7(A)): 3839–3856.

Mohd Hafiz Fazalul Rahiman, et al. 2014. A Study on Forward and Inverse Problems for an Ultrasonic Tomography. Jurnal Teknologi (Sciences & Engineering). 70(3): 113–117.

Ruzairi Abdul Rahim et al. 2014. Octagon Modelling Using Parallel Projection of Optical Tomography. Jurnal Teknologi (Sciences & Engineering). 70(3): 25–28.

Miriam Leeser, et al. 2005. Parallel-Beam Backprojection: An FPGA Implementation Optimized for Medical Imaging. Journal of VLSI Signal Processing System for Signal, Image and Video Technology. 39(3): 295–311.

Gene Frantz and Ray Simar. 2004. Comparing Fixed-Point ans Floating-Point DSPs. Texas Instruments. 1–7.

Jacub Peksinski, et al. 2012. Syncronization of Two Dimensional Digital Images Using NMSE Measure. 35th International Conference on Telecommunications and Signal Processing (TSP). 710–714.

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Published

2015-04-13

Issue

Vol. 73 No. 6: Special Issue on Sensor Technology and Control System Applications Vol. 2

Section

Science and Engineering

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Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.

How to Cite

Fixed-point Sensitivity Maps for Image Reconstruction in Tomography. (2015). Jurnal Teknologi, 73(6). https://doi.org/10.11113/jt.v73.4412