Advertisement

Implementation and Performance Assessment of Gradient Edge Detection Predictor for Reversible Compression of Biomedical Images

  • UrvashiEmail author
  • Emjee Puthooran
  • Meenakshi Sood
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)

Abstract

Technological advancement of medical imaging techniques are progressing constantly, dealing with images of increasing resolutions. In hospitals, medical imaging techniques like X-rays, magnetic resonance imaging and computed tomography etc. are of high resolution consuming large storage space. Such high resolution of medical images transmitted over the network utilizes large bandwidth that often results in degradation of image quality. So, compression of images is only a solution for efficient archival and communication of medical images. Predictive based coding technique is explored in this paper for medical image compression as it performs well for lossless compression. This paper presents a comparative investigation on 2D predictor’s coding efficiency and complexity on CT images. It was observed that among 2D predictors Gradient Edge Detection (GED) predictor gave better results than Median Edge Detector (MED) and DPCM. GED predictor at proper threshold value achieved approximately same results in terms of various performance metrics as Gradient Adaptive Predictor (GAP) though it is less complex.

Keywords

Compression Predictors Coding efficiency Complexity 

References

  1. 1.
    Gupta, M.: Low complexity near lossless image compression technique for telemedicine. Int. J. Comput. Appl. 29(7), 0975–8887 (2011)Google Scholar
  2. 2.
    Swathy, S., Jumana, N.: A study on medical image compression techniques. Int. J. Innovative Res. Comput. Commun. Eng. 5(4), 8105–8110 (2017)Google Scholar
  3. 3.
    Ghadah, K.: Fast lossless compression of medical images based on polynomial. Int. J. Comput. Appl. 70(15), 28–32 (2013)Google Scholar
  4. 4.
    Shridevi, S., Vijaykumar, V.R., Anuja, R.: A survey on various compression method for medical images. Int. J. Int. Syst. Appl. 3, 13–19 (2012)Google Scholar
  5. 5.
    Verma, P., Sahu, A., Sahu, S., Sahu, N.: Comparison between different compression and decompression techniques on MRI scan images. Int. J. Adv. Res. Comput. Eng. Technol 1(7), 109 (2012)Google Scholar
  6. 6.
    Gupta, M., Alam, S.: ROI based medical image compression for telemedicine using IWT and SPIHT. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 2(11), 340–348 (2014)Google Scholar
  7. 7.
    Chandrika, V., Parvathi, C.S., Bhaskar, P.: Medical image compression using EZW coding. IJEE. 5(2), 87–91 (2013)Google Scholar
  8. 8.
    Urvashi, Sood, M., Bhardwaj, C.: Reconstruction methods in compressive sensing for biomedical images. J. Global Pharma Technol. 6(9), 134–143 (2017)Google Scholar
  9. 9.
    Shrutika, S., Dharwadkar, N.V., Subodh, S.I.: A review on various medical image compression methods. Int. J. Innov. Res. Electr., Electron., Instrum. Control Eng. 4(1), 27–29 (2016)Google Scholar
  10. 10.
    Avramovic, A., Banjac, G.: On predictive based lossless compression of images with higher bit depth. Telfor J 4(2), 122–127 (2012)Google Scholar
  11. 11.
    Suma, Vidya, V.: A review of the effective techniques of compression in medical image processing. Int. J. Comput. Appl. 97(6), 0975–8887 (2014)Google Scholar
  12. 12.
    Avramovic, A., Reljin, B.: Gradient edge detection predictor for image lossless compression. In: 52nd International Symposium ELMAR (2010)Google Scholar
  13. 13.
    Tomar, R., Jain, K.: Lossless image compression using differential pulse code modulation and its application. Int. J. Sig. Process., Image Process. Pattern Recogn. 9(1), 197–202 (2016)Google Scholar
  14. 14.
    Al-Mahmood, H., Al-Rubaye, Z.: Lossless image compression based on predictive coding and bit plane slicing. Int. J. Comput. Appl. 93(1) (2014)Google Scholar
  15. 15.
    Avramovicl, A., Savicl, S.: Lossless predictive compression of medical images. Serblan J. Electr. Eng. 8, 27–36 (2011)CrossRefGoogle Scholar
  16. 16.
  17. 17.
  18. 18.
    Taubman, D., Marcellin, M.: JPEG2000: Image Compression Fundamentals, Standard and Practice. Kluwer Academic Publisher Group, Dordrecht (2004)Google Scholar
  19. 19.
    Wienberger M., Seroussi G.: LOCO-I; a low complexity, context based, lossless image compression algorithm. In: Conference on Data Compression. 140–149 (1996)Google Scholar
  20. 20.
    Puthooran, E., Anand, R.S., Mukherjee, S.: Lossless compression of medical images using a dual level DPCM with context adaptive switching neural network predictor. Int. J. Comput. Intell. Syst. 6(6), 1082–1093 (2013)CrossRefGoogle Scholar
  21. 21.
    Bhardwaj, C., Urvashi, Sood, M.: Implementation and performance assessment of compressed sensing for images and video signals. Int. J. Comput. Intell. Syst. 6, 123–133 (2017)Google Scholar
  22. 22.
    Weinberger, M.J., Seroussi, G., Sapiro, G.: The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS. IEEE Trans. Image Proces. 9(8), 1309–1324 (2000)CrossRefGoogle Scholar
  23. 23.
    Gopi, K., Ramashri, T.: Medical image compression using wavelets. IOSR J. VLSI Sig. Process. 4(2), 0106 (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringJaypee University of Information TechnologyWaknaghatIndia

Personalised recommendations