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ICTMI 2017 pp 53-64 | Cite as

BAT Optimization-Based Vector Quantization Algorithm for Compression of CT Medical Images

  • S. N. Kumar
  • A. Lenin FredEmail author
  • H. Ajay Kumar
  • P. Sebastin Varghese
  • Ashy V. Daniel
Conference paper

Abstract

Purpose The role of image compression is inevitable in telemedicine for the transfer of medical images. The diagnostic quality of the reconstructed image plays a vital role, and hence, the choice of compression algorithm is necessary. The medical images are analysed by the physicians for the diagnosis of anomalies. Procedures Linde Buzo Gray (LBG) is a classical algorithm for vector quantization; it produces a local optimum codebook that results in low compression efficiency. The efficiency of the vector quantization (VQ) relies on the appropriate codebook; hence, many research works based on optimization have developed for the generation of global codebook. This paper couples LBG with BAT optimization algorithm which generates an appropriate codebook. The optimization algorithm is employed not only for codebook design but also for the codebook size selection. Results The proposed BAT-LBG with dynamic codebook selection was compared with the classical LBG-VQ, JPEG lossless and BAT-LBG with static codebook selection. The algorithms were tested on abdomen CT images of five datasets, and performance evaluation was done by performance metrics like peak signal-to-noise ratio (PSNR) and mean square error (MSE). Conclusions The BAT-LBG compression with dynamic codebook size selection was found to produce efficient results when compared with JPEG lossless, classical LBG-VQ and BAT-LBG with static codebook selection. The quality of the reconstructed image was found to be good for BAT-LBG with dynamic codebook selection, and hence, it is efficient for transfer of medical images in telemedicine.

Keywords

Compression Vector quantization Codebook BAT optimization Medical images 

Abbreviations

VQ

Vector quantization

LBG

Linde Buzo Gray

GMM

Gaussian mixture modelling

TSVQ

Tree-structured vector quantization

FPSOVQ

Fuzzy inference method and particle swarm optimization

DPCM

Differential pulse code modulation

PSO

Particle swarm optimization

PSNR

Peak signal-to-noise ratio

MSE

Mean square error

CR

Compression ratio

Notes

Acknowledgements

The authors would like to acknowledge the support provided by DST under IDP scheme (No: IDP/MED/03/2015).

References

  1. 1.
    Patane G, Russo M (2002) The enhanced LBG algorithm. Neural Netw 14:1219–1237CrossRefGoogle Scholar
  2. 2.
    Elsayad AM (2003) Medical image compression using vector quantization and Guassian Mixture modelGoogle Scholar
  3. 3.
    Rajpoot A, Hussain A, Saleem K, Qureshi Q (2004) A novel image coding algorithm using ant colony system vector quantization. In: International workshop on systems, signals and image processing, Poznan, Poland, pp 13–15Google Scholar
  4. 4.
    Tsekouras GE (2005) A fuzzy vector quantization approach to image compression. Appl Math Comput 167(1):539–5605MathSciNetzbMATHGoogle Scholar
  5. 5.
    Chang CC, Li YC, Yeh JB (2006) Fast codebook search algorithms based on tree structured vector quantization. Pattern Recognit Lett 27(10):1077–1086CrossRefGoogle Scholar
  6. 6.
    Feng HM, Chen CY, Ye F (2007) Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression. Expert Syst Appl 32:213–222CrossRefGoogle Scholar
  7. 7.
    Yan Wang, Amine B, Abdesselam B, Brian Ng, FPGA implementation of a predictive vector quantization image compression algorithm for image sensor applications, IEEE International Symposium on Electoric Design, Test And Application (DELTA-2008),2008, 431–434Google Scholar
  8. 8.
    Gaudeau Y, Moureaux JM (2009) Lossy compression of volumetric medical images with 3D dead zone lattice vector quantization. Ann Telecommun 64(5):359–367CrossRefGoogle Scholar
  9. 9.
    Pal AK, Sar A (2011) An efficient codebook initialization approach for LBG algorithm. Int J Comput Sci Eng Appl (IJCSEA) 1(4)Google Scholar
  10. 10.
    Chaug Jun-Chou, Yu-chen Hu, Lo Chun-Chi (2013) Improved Mean removed vector quantization scheme for gray scale image coding. Int J Sign Proces Image Proces Pattern Recognt 6(5):315–332Google Scholar
  11. 11.
    Zhou X, Bai Y, Wang C (2015) Image compression based on discrete cosine transform and multistage vector quantization. Int J Multimedia Ubiquit Eng 10(6):347–356CrossRefGoogle Scholar
  12. 12.
    Chiranjeevi K, Umaranjan J (2016) Fast vector quantization using a Bat algorithm for image compression. Eng Sci Technol Int J 19(2):769–781CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • S. N. Kumar
    • 1
  • A. Lenin Fred
    • 2
    Email author
  • H. Ajay Kumar
    • 2
  • P. Sebastin Varghese
    • 3
  • Ashy V. Daniel
    • 2
  1. 1.Department of Electronics and Communication EngineeringSathyabama Institute of Science and TechnologyChennaiIndia
  2. 2.School of Computer Science EngineeringMar Ephraem College of Engineering and TechnologyElavuvilaiIndia
  3. 3.Metro Scans and LaboratoryTrivandrumIndia

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