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Symmetry-Based Biomedical Image Compression

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Abstract

Image compression techniques aim at reducing the amount of data needed to accurately represent an image, such that the image can be economically transmitted or archived. This paper deals with employing symmetry as a parameter for compression of biomedical images. The approach presented in this paper offers great potential in complete lossless compression of the biomedical image under consideration, with the reconstructed image being mathematically identical to the original image. The method comprises getting rid of the redundant data and encoding the non-redundant data for the purpose of regenerating the image at the receiver section without any observable change in the image data.

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References

  1. Miaou S-G, Ke F-S, Chen S-C: A lossless compression method for medical image sequences using JPEG-LS and interframe coding”. IEEE Trans Inform Technol Biomed 13(5):818–821, 2009

    Article  Google Scholar 

  2. Giuseppe Placidi, “Adaptive Compression Algorithm From Projections: Application On Medical Greyscale Images”, Journal of Computers in Biology and Medicine, Elsevier Publication, Vol 39, pp 993 – 999, 2009

  3. ‘DICOM Traffic Performance and WAAS Application Deployment Guide’, http://www.cisco.com accessed on Dec 2011.

  4. David S, Giovanni M: “Handbook of Data Compression”, 5th edition. Springer, London, 2010, pp 1–51

    Google Scholar 

  5. Digital Imaging and Communications in Medicine (DICOM), Part 5: Data Structures and Encoding PS3.5, 2014 b, http://medical.nema.org/standard.html as accessed in October 2014.

  6. V K Bairagi, A M Sapkal, “Texture Based Medical Image Compression”, Springer Journal of Digital Imaging, Vol 20, No 1, pp 65–71, Jan 2013

  7. Schmid-Saugeon P: Symmetry axis computation for almost-symmetrical and asymmetrical objects: application to pigmented skin lesions”. Med Image Anal 4(3):269–282, 2000

    Article  CAS  PubMed  Google Scholar 

  8. Gareth Loy and Jan-Olof Eklundh, “Detecting Symmetry and Symmetric Constellations of Features”, Proc. Intl Conf. ECCV, Part II, LNCS 3952, pp. 508–521, 2006.

  9. V K Bairagi, A M Sapkal, “Automated Region Based Hybrid Compression for DICOM MRI Images for Telemedicine Applications”, The IET Science, Measurement & Technology, Vol 6, No 4, pp. 247 – 253, July 2012

  10. V K Bairagi, A M Sapkal, M S Gaikwad, “The Role of Transforms in Image Compression”, Springer Journal of Institute of Engineers (India), Series B., Vol 94, No 2, pp 135–140, June 2013

  11. The Whole Brain – Atlas [Online]. Available, http://www.med.harvard.edu/AANLIB/home.htm. accessed Dec 2011

  12. Computer vision group [Online]. Available, http://decsai.ugr.es/cvg/index2.php. accessed Aug 2011

  13. The National Library of Medicine (NLM) [Online]. Available, http://www.nlm.nih.gov. accessed Jul 2011.

  14. Sanchez V, Abugharbieh R, Nasiopoulos P: Symmetry-based scalable lossless compression of 3D medical image data”. IEEE Trans Med Imaging 28(7):1062–1072, 2009

    Article  CAS  PubMed  Google Scholar 

  15. V. Sanchez, R. Abugharbieh and P. Nasiopoulos, “3D scalable lossless compression of medical images based on global and local symmetries”, Proc. 16th IEEE Intl Conf. ICIP, pp. 2525–2528, Nov 2009.

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Acknowledgments

The author would like to thank the University of Pune, India for financially supporting this work under research grant and the Sinhgad General Hospital, Pune for their valuable help and support. The author would like to thank all authors of the references which have been used, as well as reviewers of the paper

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Authors and Affiliations

  1. Department of E&TC, AISSMS’s Institute of Information Technology, Pune, India

    V. K. Bairagi

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  1. V. K. Bairagi
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Correspondence to V. K. Bairagi.

Additional information

Manuscript received on 4 April 2014 revised on 21st December 2014. This work was supported in part by the University of Pune under BCUD research grant.

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Bairagi, V.K. Symmetry-Based Biomedical Image Compression. J Digit Imaging 28, 718–726 (2015). https://doi.org/10.1007/s10278-015-9779-3

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