Hybrid image compression technique using oscillation concept & quasi fractal

  • Satyawati S. MagarEmail author
  • Bhavani Sridharan
Original Paper
Part of the following topical collections:
  1. Internet Of Medical Things In E-Health


In medical images, especially for brain images ROI is very important for diagnosis. ROI is very important compare to other portion of an image. Here ROI is included in hybrid coding algorithm for effective image compression. Compression method gives better results using hybrid algorithm. In this paper, we have used hybrid compression method, Lossless used for ROI portion and for non-ROI portion the lossy compression techniques has been used. The experimental results shows that better Compression Ratio (CR) with acceptable PSNR has been achieved using hybrid technique based on Morphological band pass filter and Adaptive thresholding for ROI.


Morphological filter Adaptive thresholding Hybrid technique Oscillation concept BTC-SPIHT ROI Quasi-fractal 


Compliance with ethical standards

Our work is not funded by any agencies or organization.

Conflict of interest

None of the author received fund from any agencies or committee or organization.


  1. 1.
    Magar S, Sridharan B. Innovative approach to biomedical image compression using oscillation concept. International Conference on Automotive Control and Dynamic Optimization techniques (ICACDOT), pp. 124-128, IEEE Conference Publication-IEEE Xplore, 2016.Google Scholar
  2. 2.
    Magar S, Sridharan B. Comparative analysis of biomedical image compression using oscillation concept and existing method. Lecture Notes in Computational Vision and Biomechanics, Book Series, volume 28, Springer.Google Scholar
  3. 3.
    Chaudhary RN. Waves and oscillations. New Edge International Publishers.Google Scholar
  4. 4.
    Mohan Singh G, Singh Kohliy M and Diwakarz M. A review of image enhancement techniques in image processing. HCTL Open Int. J. of Technology Innovations and Research, HCTL Open IJTIR. 2013; 5: ISBN:978-1-62840-986-4.Google Scholar
  5. 5.
    Bansal V, Gupta P, Purohit GN. Block truncation encoding for image compression technique. Int J Emerg Res Manag Technol ISSN: 2278-9359 (Volume-4, Issue-4).Google Scholar
  6. 6.
    Nirmal Raj S. SPIHT: a set partitioning in hierarchical trees algorithm for image compression. Contemporary Engineering Sciences. 2015;8(6):263–70.CrossRefGoogle Scholar
  7. 7.
    Basavanthaswami V, Somasekhar T. “Image compression using SPIHT” International Journal of Innovative Research in Computer and Communication Engineering. 2017; 5(2).Google Scholar
  8. 8.
    Kumar T, Kumar R. Medical image compression using hybrid techniques of DWT, DCT and Huffman coding. Int J Innov Res Electr Electron Instrum Control Eng. 2015;3(2)Google Scholar
  9. 9.
    Bhavani S, Thanushkodi KG. Compression of fractal coding methods for medical image compression. IET Image Process. 2013;7:686–93.CrossRefGoogle Scholar
  10. 10.
    Sridhanran B, Thanushkodi K. Improving the performance of fractal based quasi lossless medical image coding scheme using machine learning based partition and domain range pools. Eur J Sci Res. 2012;68:475–86.Google Scholar
  11. 11.
    BalaAnand M, Karthikeyan N, Karthik S. Designing a framework for communal software: based on the assessment using relation modelling. Int J Parallel Prog. 2018.
  12. 12.
    Rathkanthiwar SV, Kakde S, Naaz H. Implementation of Hybrid Algorithm for Image Compression and Decompression. International Journal of Engineering Research. 2016;5(5):398–403.Google Scholar
  13. 13.
    Joshi P, Rawat CD. Region based hybrid compression for medical images. International conference on signal processing, Communication, Power and Embedded System (SCOPES), IEEE Xplore. 2016.Google Scholar
  14. 14.
    Cheng J, Dong Y, Park S. Detecting region-of-interest (ROI) in digital mammogram by using morphological bandpass filter. IEEE International Conference on Multimedia and Expo, 2004; ICME’04.Google Scholar
  15. 15.
    Ping W, Zhao Shanxu LJ, Dongning L, Gang C. A method of detection micro-clacification in mammograms using wavelets and adaptive thresholds. IEEE 2008, pp 2361-2364.Google Scholar
  16. 16.
    Francesco GB, Natale DE, Boato G. Detecting morphological filtering of binary images. IEEE Transaction on Information Forensics and Security. 2017;12(5):1207–17.CrossRefGoogle Scholar
  17. 17.
    Roy P, Dey G. Adaptive thresholding: a comparative study. 2014 International conference on control, Instrumentation, communication and Computational Technologies (ICCICCT).Google Scholar
  18. 18.
    Chaphman S. Matlab programming for engineers. Cengage Learning Publishers.Google Scholar

Copyright information

© IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of ECEKarpagam Academy of Higher EducationCoimbatoreIndia

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