Computed tomogram image encoding for internet of things based systems

  • Ali Akbar Siddique Department of Telecommunication Engineering, Sir Syed University of Engineering & Technology
  • M. Tahir Qadri Department of Electronics Engineering, Sir Syed University of Engineering & Technology
  • Zia Mohy-Ud-din Department of Biomedical Engineering, Sir Syed University of Engineering & Technology
Keywords: Internet of Things, Image compression, Discrete Cosine Transform, Quantization, Encoding

Abstract

Background: In recent times, transmission of information over a wireless channel is increasing at an exponential rate. Applications based on IOT will exceed all form of data available over the internet, it can be in the form of data based on videos and images which alone size up to 70% of the global traffic.

Methodology: In this paper, an encoding technique based on discrete cosine transform (DCT) have been utilized to reduce information of a CT image that is unwanted or a human eye is unable to perceive. For medical images, preserving maximum information in order to properly diagnose any disease is important and for such application, the amount of quantization needed to gain an image with minimum error is to observe the Correlation index (CI).

Results: CI at 50% Quality Quantization Table (QQT) for image 1 was found to be 0.9981 and its size reduced to 0.403 MB, it means that the compressed image is 99.8% similar to the original image. But its similarity reduces with the increase in QQT, its size will also decrease by at this point quality will degrade too.

Conclusion: In order to store images on the drive, it is imperative to apply a compression technique. In this paper, a compression technique is proposed utilizing a DCT algorithm to reduce an image size with different QQT provided for quality assessment. Each image has a different set of information and to cater their differences this QQT can be manipulated for best possible quality with acceptable size on disk.

References

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Brenner DJ, Hall EJ. Computed tomography—an increasing source of radiation exposure. N Engl J Med. 2007; 357(22):2277-2284.

European Society of Radiology 2009. The future role of radiology in healthcare. Insights Imaging. 2010; 1(1):2-11.

Johnston N, Vincent D, Minnen D, Covell M, Singh S, Chinen T, Hwang SJ, Shor J, Toderici G. Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks. Structure. 2017; 10: 23.

Ridenour RL, Frederiksen JE, Hendry IC, inventors; Apple Inc, assignee. Lossless image compression using differential transfer. United States patent US 9,386,318. 2016. 2018.

Published
2018-12-01
How to Cite
Siddique, A. A., Qadri, M. T., & Mohy-Ud-din, Z. (2018). Computed tomogram image encoding for internet of things based systems. International Journal of Endorsing Health Science Research (IJEHSR), 6(4), 08-18. https://doi.org/10.29052/IJEHSR.v6.i4.2018.08-18