Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Medical Image Compression using Adaptive Subband Threshold

  • Vidhya, K (Dept. of Electronics and Communication Engineering, Vel Tech)
  • Received : 2014.11.13
  • Accepted : 2015.09.28
  • Published : 2016.03.01
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Abstract

Medical imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Ultrasound (US) produce a large amount of digital medical images. Hence, compression of digital images becomes essential and is very much desired in medical applications to solve both storage and transmission problems. But at the same time, an efficient image compression scheme that reduces the size of medical images without sacrificing diagnostic information is required. This paper proposes a novel threshold-based medical image compression algorithm to reduce the size of the medical image without degradation in the diagnostic information. This algorithm discusses a novel type of thresholding to maximize Compression Ratio (CR) without sacrificing diagnostic information. The compression algorithm is designed to get image with high optimum compression efficiency and also with high fidelity, especially for Peak Signal to Noise Ratio (PSNR) greater than or equal to 36 dB. This value of PSNR is chosen because it has been suggested by previous researchers that medical images, if have PSNR from 30 dB to 50 dB, will retain diagnostic information. The compression algorithm utilizes one-level wavelet decomposition with threshold-based coefficient selection.

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References

  1. Chen Y.Y., “Medical image compression using DCT-based subband decomposition and modified SPIHT data organization,” Int. J. Med. Informat., vol. 76, no.10, pp. 717-725, 2007. https://doi.org/10.1016/j.ijmedinf.2006.07.002
  2. Sikora T., "Trends and perspectives in image and video Coding," in Proc. IEEE, vol. 93, no. 1, pp. 6-17, 2005. https://doi.org/10.1109/JPROC.2004.839601
  3. Khalid Sayood, Introduction to data compression, Morgan Kaufmann Publications, 2006.
  4. Singla V., Singla R. and Gupta S., “Data compression modeling: Huffman and Arithmetic,” International Journal of The Computer, The Internet and Management, vol. 16, no. 3, pp. 64-68, 2008.
  5. Rehna V.J. and Jeya Kumar M.K., “Wavelet based image coding schemes: A recent survey,” International Journal on Soft Computing, vol. 3, no. 3, 2012.
  6. Prabhakar T., Jagan Naveen V., Lakshmi A., Prasanthi G. and Vijaya Santhi G., “Image compression using DCT and wavelet transformations,” International Journal of Signal Processing, Image Processing and Pattern Recognition,” vol.4, no. 3, 2011.
  7. Rani, B., Bansal, R.K. and Bansal, S. “Comparison of JPEG and SPIHT image compression algorithms using objective quality measures”, in Proc. IEEE International Multimedia Signal Processing and Communication Technologies, pp. 90-93, 2009.
  8. Taubman D.S., “High performance scalable image compression with EBCOT,” IEEE Trans. Image Process., vol. 9, no. 7, pp. 1158-1170, 2000. https://doi.org/10.1109/83.847830
  9. Taubman D.S. and Marcellin M.W., JPEG2000: Image compression fundamentals, standards and practice, Kluwer Academic Publishers, Boston, USA, 2002.
  10. Koff D.A. and Shulman H., “An overview of digital compression of medical images: Can we use lossy image compression in Radiology?,” Can. Assoc. Radiol. J., vol. 57, no. 4, pp. 211-217, 2006.
  11. Sone S.,Takashima S., Kiyono K., Yang Z.G., Hasegawa M., Kawakami S., Saito A., Hanamura K. and Asakura K., “Effects of JPEG and wavelet compression of spiral low-dose CT images on detection of small lung cancers,” Acta Radiol., vol. 42, no. 2, pp. 156-160, 2001. https://doi.org/10.1080/028418501127346657
  12. Chen Y.Y., “Medical image compression using DCT-based subband decomposition and modified SPIHT data organization,” Int. J. Med. Informat., vol. 76, no. 10, pp. 717-725, 2007. https://doi.org/10.1016/j.ijmedinf.2006.07.002
  13. Choong M.K., Logeswaran R. and Bister M., “Cost-effective handling of digital medical images in the telemedicine environment,” Int. J. Med. Informat., vol. 26, no. 9, pp. 646-654, 2007.
  14. Persons K.R., Palisson P.M., Manduca A., Charboneau W.J., James E.M., Charboneau N.T., Hangiandreou N.J. and Erickson B.J., “Ultrasound gray scale image compression with JPEG and wavelet techniques,” J. Digit. Imag., vol. 13, no. 1, pp. 25-32, 2000. https://doi.org/10.1007/BF03168337
  15. Li F., Sone S., Takashima S., Kiyono K., Yang Z.G., Hasegawa M., Kawakami S., Saito A., Hanamura K. and Asakura K., “Effects of JPEG and wavelet compression of spiral low-dose CT images on detection of small lung cancers,” Acta Radiol., vol. 42, no. 2, pp. 156-160, 2001. https://doi.org/10.1080/028418501127346657
  16. Asraf R., Akbar M. and Jafri N., “Statistical analysis of different image for absolute lossless compression of medical images,” in Proc. IEEE Eng. Med. Biol. Soc., pp.4767-4770, 2006.
  17. Tahoces P.G., Varela J.R., Lado M.J. and Souto M., “Image compression: Maxshift ROI encoding options in JPEG2000,” Comput. Vis. Image Understand., vol. 109, pp. 139-145, 2008. https://doi.org/10.1016/j.cviu.2007.07.001
  18. Dragan D. and Ivetic D., “A comprehensive quality evaluation system for PACS,” UBICC journal, vol. 4, no. 3, pp. 642-650, 2009.
  19. Miaou S.G. and Chen S.T., “Automatic quality control for wavelet based compression of volumetric medical images using distortion constrained adaptive vector quantization,” IEEE Trans. Med. Image, vol. 23, no. 11, 2004.
  20. Lin Z., Hoffman M.W., Leon W.D., Schemm N. and Balkir S., “A CMOS image sensor with focal plane SPIHT image compression,” in Proc. IEEE Int. Symp. on Circuits and Systems, pp. 2134-2137, 2008.
  21. Mastriani M., “Union is strength in lossy image compression,” Int. J. Inform. Comm. Eng., vol. 5, no. 2, pp. 102-119, 2009.
  22. Kafri N. and Sulieman H.Y., “Bit-4 of frequency domain-DCT stenography technique,” in Proc. IEEE Int. Conf. on Networked Digital Technologies, pp. 286-291, 2009.
  23. Russakoff D.B., Rohlfing T., Mori K. and Rueckert D., Ho A., Adler J.R. and Maurer C.R., “Fast generation of digitally reconstructed radiographs using attenuation fields with application to 2D-3D image registration,” IEEE Trans. Med. Imag., vol. 24, no. 11, pp. 1441-1454, 2005. https://doi.org/10.1109/TMI.2005.856749
  24. El Safy R.O., Zayed H.H., El Dessouki A., “An adaptive steganographic technique based on integer wavelet transform,” in IEEE Proc., Int. Conf. on Networking and Media Convergence, pp. 111-117, 2009.
  25. Ghasemi E., Shanbehzadeh J. and Fassihi N., “High capacity image steganography using wavelet transform and genetic algorithm,” in Proc. Int. Multiconference of Engineers and Computer Scientists, vol. 1, 2011.
  26. Shahbahrami A., Bahrampour R., Rostami M. and Mobarhan M., “Evaluation of Huffman and Arithmetic algorithms for multimedia compression standards,” Int. J. Comput. Sci. Eng. Appl., vol. 1, no. 4, pp. 34-47, 2011. https://doi.org/10.5121/ijcsea.2011.1404
  27. Wang Z. and Bovik A.C., “A Universal image quality index,” IEEE Signal Process. Lett., 2002.
  28. Wang Z., Bovik A.C., Sheikh H.R. and Simoncelli, E.P., “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, 2004. https://doi.org/10.1109/TIP.2003.819861
  29. Maly J. and Rajmic P., DWT-SPIHT image codec implementation, Brno Univ. of Tech., Crech Republic, 2007.
  30. Saffor A., Ramli A., Ng K. and Dowsett K., “Objective and subjective evaluation of compressed Computed Tomography (CT) images,” Internet J. Radiol., vol.2, no. 2, 2002.
  31. Ghrare S.E., Ali M.A.M., Ismail M. and Jumari K., “The effect of image data compression on the clinical information quality of compressed computed tomography images for teleradiology applications,” Eur. J. Sci. Res., vol. 23, no. 1, pp. 6-12, 2008.

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