Advertisement

Cluster Computing

, Volume 22, Supplement 5, pp 12805–12816 | Cite as

Hybrid Lempel–Ziv–Welch and clipped histogram equalization based medical image compression

  • M. A. P. ManimekalaiEmail author
  • N. A. Vasanthi
Article

Abstract

Nowadays, the medical images upsurge because of numerous major disease predictions. The medical image size needed vast volumes of memory and taking additional bandwidth for storage as well as transmission. With the aim of decreasing the size of the storage and as well for greater transmission image compression is needed. The prior research presented a completely automatic technique for skin lesion segmentation by leveraging 19-layer deep convolutional neural networks (CNNs), which is skilled end to-end and does not depend on past acquittance of the data. On the other hand it contains problem with storage for the period of the medical image transmission and transmission speed. With the aim of resolving this issue the presented system developed a compression method. In this research, magnetic resonance imaging are pre-processed by means of median filter. The preprocessed image is split into region of interest (ROI) and non region of interest by means of deep fully convolutional networks with Jaccard distance. Subsequent to the ROI segmentation, the ROI edge is taken out and encrypted with Freeman chain coding. At that point the ROI part is compressed by hybrid Lempel–Ziv–Welch and clipped histogram equalization (CHE). In CHE, ideal threshold value is chosen by means of particle swarm optimization technique for enhancing the brightness maintenance. The Non ROI part is compressed by means of enhanced zero tree wavelet (EZW). In this EZW technique, a preliminary threshold is chosen by making use of firefly algorithm. Lastly the decompression is carried out at the receiving end. The experimentation outcomes prove that the presented method attains greater performance when matched up with the previous technique in regard to compression ratio, peak signal to noise ratio and mean square error.

Keywords

Region of interest MRI image Lossless compression and enhanced zero tree wavelet (EZW) 

References

  1. 1.
    Pinto, S.J., Gawande, J.P.: Performance analysis of medical image compression techniques. In: Proceedings of the Institute of Electrical and Electronics Engineers IEEE (2012)Google Scholar
  2. 2.
    Poobal, S., Ravindran, G.: The performance of fractal image compression on different imaging modalities using objective quality measures. In: Proceedings of the International Journal of Science and Engineering Technology (IJEST 2011), vol. 3(1), pp. 525–530 (2011)Google Scholar
  3. 3.
    Vijayvargiya, G., Silakari, S., Pandey, R.: A novel medical image compression techniques based on structure reference selection using integer wavelet transform function and PSO algorithm. Int. J. Comput. Appl. 91, 11 (2014)Google Scholar
  4. 4.
    Sriraam, N., Shyamsunder, R.: 3D Medical Image Compression Using 3D Wavelet Coders, vol. 16, pp. 100–109. Elsevier on Digital Image Processing, New York (2010)Google Scholar
  5. 5.
    Ferni Ukrit, M., Umamageswari, A., Suresh, G.R.: A survey on lossless compression for medical image. Int. J. Comput. Appl. 31, 8 (2011)Google Scholar
  6. 6.
    Singh, H., Sharma, S.: Hybrid image compression using DWT, DCT & Huffman encoding techniques. Int. J. Emerging Technol. Adv. Eng. 2, 300–306 (2012)Google Scholar
  7. 7.
    Asghari, M.H., Bahram, J.: Big data compression using anamorphic stretch transform. In: Asebigdata/socialcom/Cyber Security Conference, May 27–31. Stanford University, Stanford (2014)Google Scholar
  8. 8.
    Chaabouni, I., Bouhlel, M.S.: Toward an Optimal Medical Image Compression Based on ISOM. Piscataway, IEEE (2015)CrossRefGoogle Scholar
  9. 9.
    Reddy, B.V., Reddy, P.B., Kumar, P.S., Reddy, A.S.: Lossless compression of medical images for better diagnosis. In: IEEE 6th International Conference on Advanced Computing (2016)Google Scholar
  10. 10.
    Xiong, Z., Sun, X., Wu, F.: Block-based image compression with parameter-assistant inpainting. IEEE Trans. Image Process. 19, 6 (2010)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Jain, D.K., Gaur, D., Gaur, K., Jain, N.: Image compression using discrete cosine transform and adaptive huffman coding. Int. J. Emerging Trends Technol. Comput. Sci. (IJETTCS) 3, 1 (2014)Google Scholar
  12. 12.
    Ruchika, M.S., Singh, A.R.: Compression of medical images using wavelet transforms. Int. J. Soft Comput. Eng. (IJSCE) 2, 2 (2012). ISSN: 2231-2307Google Scholar
  13. 13.
    Lucas, L.F., Rodrigues, N.M., da Silva Cruz, L.A., de Faria, S.M.: Lossless Compression of Medical Images Using 3D Predictors”. IEEE, New York (2016)Google Scholar
  14. 14.
    Saurin, P., Ruiz, D., Kalva, H., Fernández-Escribano, G., Adzic, V.: High Bit-Depth Medical Image Compression with HEVC. IEEE J. Biomed. Health Inf. 1(99), 1 (2016)Google Scholar
  15. 15.
    Pang, J., Zhang, S., Zhang, S.: A median filter based on the proportion of the image variance. In: Information Technology, Networking, Electronic and Automation Control Conference, IEEE, pp. 123–127. IEEE (2016)Google Scholar
  16. 16.
    Abbas, Q.: A software approach for border detection using pigmented skin lesions. IJCSNS 17(5), 231 (2017)Google Scholar
  17. 17.
    Tang, S.: Stochastic gradient descent (2017)Google Scholar
  18. 18.
    Xi, C., Zongze, W., Xie, Z., Youjun, X., Shengli, X.: One novel rate control scheme for region of interest coding. In: International Conference on Intelligent Computing, pp. 139–148. Springer (2016)Google Scholar
  19. 19.
    MR, A. D., Ahamad, M. G., Ravichandran, D.: Medical image compression using embedded zerotree wavelet (EZW) coder. In: International Conference on System Modeling & Advancement in Research Trends (SMART), pp. 17–23. IEEE (2016)Google Scholar
  20. 20.
    Lei, X., Wang, F., Wu, F.X., Zhang, A., Pedrycz, W.: Protein complex identification through Markov clustering with firefly algorithm on dynamic protein-protein interaction networks. Inf. Sci. 329, 303–316 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringKarunya UniversityCoimbatoreIndia
  2. 2.Department of Information TechnologyDr.N.G.P. Institute of TechnologyCoimbatoreIndia

Personalised recommendations