Brain Image Compression Techniques

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2015 by IJETT Journal
Volume-19 Number-2
Year of Publication : 2015
Authors : M.Abo–Zahhad , R.R.Gharieb , Sabah M.Ahmed , Mahmoud Khaled
DOI :  10.14445/22315381/IJETT-V19P218

Citation 

M.Abo–Zahhad , R.R.Gharieb , Sabah M.Ahmed , Mahmoud Khaled "Brain Image Compression Techniques", International Journal of Engineering Trends and Technology (IJETT), V19(2),93-105 Jan 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

This paper presents a survey of different methods in the prospect of medical and brain image compression. Compressing an image is basically a process of encoding the image to reduce the size of image as a number of bytes for storage and transmission purposes. This should be done while preserving as much as possible the quality of the image. Hence, image compression has been founded necessary in different medical imaging systems, where there are too much number of bytes of the reconstructed images. There are basically two categories of compression techniques; namely lossless and lossy compression techniques. As the name indicates, in the lossless technique the image is encoded without any loss of information. But in the lossy one, it is allowed that some information is missed. The lossy compression techniques are commonly applied to multimedia data such as audio, video, and still images. Several lossless and lossy compression approaches have been applied to medical images. Different compression approaches belonging to the two categories are discussed and Brain images compression techniques are highlighted. Furthermore, quantitative comparisons between different compression methods are given.as well as advantages and disadvantages of each method.

References

[1] Hammudy, H. H., "Image Compression Using Multiwavelet Transform", Iraqi Commission For Computer And Information Institute For Postgraduate Studies, M.Sc. Thesis, Jan 2006.
[2] SalehaMasood, Muhammad Sharif, MussaratYasmin, MudassarRaza and SajjadMohsin. Brain Image Compression: A Brief Survey. Engineering and Technology 5(1): 49-59, 2013.
[3] M.Pratap, N. Kumar, “An Optimized Lossless Image Compression Technique In Image Processing” Institute of Technology Roorkee, Dec. 2012.
[4] Huda Mahmood, “Lossless Image Compression Using Prediction Coding and LZW Scheme”, Submitted to College of Science of Baghdad University. 2011.
[5] S. Kumar, T. U. Paul, A. Raychoudhury, “Image Compression using Approximate Matching and Run Length”, International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, 2011.
[6] Al-Khoja, S.B., “Wavelet Compression Using Tree and Adaptive Arithmetic Codes”, University of Baghdad, M.Sc. Thesis, 2004.
[7] A. B. Watson, ‘‘Image Compression Using the Discrete Cosine Transform’’, Mathematica Journal, 4(1), pp. 81-88, 1994.
[8] Brian A. Wandell, “Foundations of Vision”, “https://foundationsofvision.stanford.edu/ chapter-8-multiresolution-image-representations/”, 2014, [accessed 31/12/2014].
[9] A. Jalobeanu, L. Blanc-Feraud, and J. Zerubia.Satellite Image Deconvolution Using Complex Wavelet Packets. Technical report, Institute National de Recherché en Informatiqueet en Automatique, France, 2000.
[10] Y. Linde, A. Buzo, and R. M. Gray, “An Algorithm for Vector Quantizer Design,” IEEE Trans. Communications, vol. 28, no. 1, pp. 84–95, January 1980.
[11] G. Poggi and E. Sasso, “Codebook Ordering Techniques for Address Predictive VQ,” in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing ’93, Minneapolis, April 27-30 1993, pp. 586–589.
[12] H. Liu and D. Y. Y. Yun, “Self-organizing Finite State Vector Quantization for Image Coding,” in Proc. of the Int. Workshop on Applications of Neural Networks to Telecommunications, J. Alspector, R. Goodman, and T. X. Brown, Eds. Hillsdale, NJ: Lawrence Erlbaum Associates, 1993, pp. 176–182.
[13] T. Kohonen, “The Self-organizing Map,” Proc. IEEE, vol. 78, no. 9, pp. 1464–1480, September 1990.
[14] J. Li and C. N. Manikopoulos, “Multi-stage Vector Quantization Based on the Self-organization Feature Maps,” in SPIE Vol. 1199 Visual Communications and Image Processing IV (1989), 1989, pp. 1046–1055.
[15] S. P. Luttrell, “Hierarchical Vector Quantization,” IEE Proceedings (London), vol. 136 (Part I), pp. 405–413, 1989.
[16] T.-C. Lee and A. M. Peterson, “Adaptive Vector Quantization Using a Self-development Neural Network,” IEEE J. on Selected Areas in Communications, vol. 8, no. 8, pp. 1458–1471, Oct. 1990.
[17] Ajit Singh, Meenakshi Gahlawat, " Image Compression and its Various Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 6, June 2013.
[18] Wang, J. and K. Huang, 1996. Medical Image Compression by Using Three-dimensional Wavelet Transformation. Med. Imaging, IEEE Trans., 15(4): 547-554
[19] Rodet, T., P. Grangeat and L. Desbat, 2000. A New Computation Compression Scheme Based on a Multi Frequential approach. Nuclear Science Symposium Conference Record, 2000 IEEE, vol.2, pp. 15/267-15/271.
[20] Hashimoto, M., K. Matsuo, A. Koike, H. Hayashi and T. Shimono, 2004. “CT Image Compression with Level of Interest Image Processing”, 2004.ICIP `04. 2004 International Conference, 5, pp. 3185- 3188
[21] Kanoun, O., M.S. Bouhlel and S. Mezghani, 2006. “Medical Images Adaptive Compression With Statistical Model for Transmission and Archiving”, Application to MRI modality. Information and Communication Technologies, 2006. ICTTA `06.2nd, vol.1, pp. 1457-1462.
[22] Shaou-Gang, M., K. Fu-Sheng and C. Shu-Ching, "A Loss less Compression Method for Medical Image Sequences Using JPEG-L Sand Interframe Coding”, IEEE 2009.
[23] Tzong-Jer, C. and C. Keh-Shih, 2010. A Pseudo Lossless Image Compression Method. Image and Signal Processing (CISP), 2010 3rd International Congress, vol.2, pp. 610-615.
[24] Vaishali G. Dubey, Jaspal Singh, “3D Medical Image Compression Using Huffman Encoding Technique”, ECE Department, RIEIT Railmajra, SBSNagar, Punjab, International Journal of Scientific and Research Publications, Volume 2, Issue 9, September 2012
[25] Anju B, Manimurugan S, “An Approach to Medical Image Compression Using Filters Based on Lifting Scheme”, IOSR Journal of VLSI and Signal Processing, vol. 1, no. 2 pp. 9-16, Sep-Oct. 2012.
[26] Sandeep Kumar, NitinGoel, Vedpal Singh, AmitChaudhary, NirmalSirohi, Gurbaj Singh, “Fast and Efficient Medical Image Compression Using Contourlet Transform: (FEMI-CCT)”, Ganpati Institute of Technology & Management, India, Open Journal of Computer Sciences, Vol.1, No.1, 7-13, May, 2013
[27] Yilmaz, R. and I. Kilic, 1998. Hierarchical Finite State Vector Quantization for MRI and CT Image Compression. Electrotechnical Conf., 1998. MELECON 98, 9th Mediterranean, vol.1, pp. 77-81.
[28] Midtvik, M. and I. Hovig, 1999. Reversible Compression of MR Images. Medical Imaging, IEEE Trans., 18(9): 795-800.
[29] Cavaro-Menard, C., A. Le Duff, P. Balzer, B. Denizot, O. Morel, P. Jallet and J.J. Le Jeune, Quality Assessment of Compressed Cardiac MRI. Effect of lossy compression on computerized physiological parameters," Inter. Conf., on Image Analysis and Processing, pp. 1034-1037, 1999.
[30] Dhouib, D., A. Nait-Ali, C. Olivier and M.S. Naceur, 1994. Comparison of Wavelet Based Coders Applied to 3D Brain Tumor MRI Images. Systems, Signals and Devices, 2009.SSD`09. 6th International Multi- Conference, pp. 1-6, 23-26 March 2009.
[31] Badawy, W., M. Weeks, Z. Guoqing, M. Talley and M.A. Bayoumi, 2002. “MRI Data Compression Using a 3-D Discrete Wavelet Transform”. Eng. Med. Bio. Magazine, IEEE, 21(4): 95-103.
[32] Karras, D.A., 2009. “Compression of MRI Images Using the Discrete Wavelet Transform and Improved Parameter Free Bayesian Restoration Techniques”. Imaging Systems and Techniques, IEEE Inter. Workshop, pp. 173-178, 2009.
[33] Hu, L.L., F. Zhang, Z. Wang, X.F. You, L. Nie, H.X. Wang, T. Song and W.H. Yang, Comparison of the 1HR Relaxation Enhancement Produced by Bacterial Magnetosomes and Synthetic Iron Oxide Nanoparticles for Potential Use as MR Molecular Probes. Appl. Supercond., IEEE Trans., 20(3), pp. 822-825, 2010.
[34] Gornale, S.S., V.T. Humbe, S.S. Jambhorkar, P. Yannawar, R.R. Manza and K.V. Kale, 2007. Multi-Resolution System for MRI (Magnetic Resonance Imaging) Image Compression: A Heterogeneous Wavelet Filters Bank Approach.Computer Graphics, Imaging and Visualization, 2007. CGIV `07, pp. 495-500.
[35] Li, G., J. Zhang, Q. Wang, C. Hu, N. Deng and J. Li, 2006. Application of Region Selective Embedded Zerotree Wavelet Coder in CT Image Compression. Engineering in Medicine and Biology Society, 2005.IEEE-EMBS 2005. 27th Annual International Conference, pp. 6591-6594.
[36] Corvetto, A., A. Ruedin and D. Acevedo, 2010. Robust Detection and Lossless Compression of the Foreground in Magnetic Resonance Images. Data Compression Conference (DCC), pp. 529.
[37] G. Soundarya and S. Bhavani, “Comparison of Hybrid Codes for MRI Brain Image Compression”,Maxwell Scientific Organization, December 15, 2012
[38] Alagendran .B, S. Manimurugan , M. John Justin, “Compression of 3D Medical Image Using EDGE Preservation Technique”, International Journal of Electronics and Computer Science Engineering, pp. 802-809, 2012
[39] Seddeq E. Ghrare, and Salahaddin M. Shreef, “Proposed Quality Evaluation of Compressed MRI Medical Images for Telemedicine Applications”, World Academy of Science, Engineering and Technology 2012, pp. 568-570
[40] Rhodes, M.L., J.F. Quinn and J. Silvester, 1985. Locally Optimal Run-Length Compression Applied to CT Images. Med. Imaging, IEEE Trans., 4(2): 84-90.
[41] Lee, H., Y. Kim, A.H. Rowberg and E.A. Riskin, 1991. 3-D Image Compression for X-ray CT Images Using Displacement Estimation. Data Compression Conference, 1991.DCC `91. pp. 453.
[42] Ju, L.O. and A.K. Seghouane, 2009. False Positive Reduction in CT Colonography Using Spectral Compression and Curvature Tensor Smoothing of Surface Geometry. Biomedical Imaging: From Nano to Macro, 2009. ISBI `09. IEEE International Symposium, pp. 89-92.
[43] Signoroni, A., S. Masneri, A. Riccardi and I. Castiglioni, 2009. Enabling Solutions for an Efficient Compression of PET-CT Datasets. Nuclear Science Symposium Conference Record (NSS/MIC), 2009 IEEE, pp. 2747-2751.
[44] Sepehrband, F., M. Mortazavi and S. Ghorshi, 2010. Efficient DPCM Predictor for Hardware Implementation of Lossless Medical Brain CT Image Compression. Signals and Electronic Systems (ICSES), 2010 International Conference, pp. 123-126.
[45] S. Kumar1, N. Goel2, V. Singh3, A. Chaudhary4, N. Sirohi5, G. Singh, “Fast and Efficient Medical Image Compression Using Contourlet Transform”, Open Journal of Computer Sciences, Vol.1, No.1, pp. 7-13, May, 2013.
[46] Weidong, C., F. Dagan and R. Fulton, 1998. “Clinical Investigation of a Knowledge-Based Data Compression Algorithm for Dynamic Neurologic FDG-PET Images”. Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE, vol.3, pp. 1270-1273.
[47] Dahlbom, M., K.A. Gardner, A. Chatziioannou and C.K. Hoh, 1994. Whole Body PET Image Compression. Nuclear Science Symposium and Medical Imaging Conference, 1994. IEEE Conference Record, vol. 3, pp. 1394-1398.
[48] Macq, B., M. Sibomana, A. Coppens, A. Bol, C. Michel, K. Baker and B. Jones, 1994. “Lossless Compression for 3D PET”. Nucl. Sci., IEEE Trans., 41(4): 1556-1559.
[49] Min-Jen, T., J.D. Villasenor, A. Chatziioannou and M. Dahlbom, 1995. Positron Emission Tomography Compression by Using Wavelet Transform. Nuclear Science Symposium and Medical Imaging Conference Record, 1995, 1995 IEEE, vol.3, pp. 1434-1437.
[50] Zhe, C., B. Parker and D.D. Feng, 2003. “Temporal Compression for Dynamic Positron Emission Tomography via Principal Component Analysis in the Sinogram Domain”. Nuclear Science Symposium Conference Record, 2003 IEEE, vol. 4, pp. 2858-2862.
[51] Panins, V.Y., 2008. “Iterative Algorithms for Variance Reduction on Compressed Sinogram Random Coincidences in PET”. Nuclear Science Symposium Conference Record, 2008.NSS `08. IEEE, pp. 3719-3725.
[52] Ashish Raj, AkankshaDeo, Mangesh S. Tomar, Manoj Kumar Bandil, “Analysing the Inclusion of Soft Computing Techniques in Denoising EEG Signal”, International Journal of Soft Computing and Engineering, Volume-2, Issue-4, September 2012.
[53] Madan, T., R. Agarwal and M.N.S. Swamy, 2004. Compression of Long-term EEG Using Power Spectral Density. Engineering in Medicine and Biology Society, 2004.IEMBS `04. 26th Annual International Conference of the IEEE, pp. 180-183.
[54] Sriraam, N., 2007. “Neural Network Based Near- Lossless Compression of EEG Signals with Non Uniform Quantization”. Engineering in Medicine and Biology Society, 2007.EMBS 2007. 29th Annual International Conference of the IEEE, pp. 3236-3240.
[55] Aviyente, S., 2007. “Compressed Sensing Framework for EEG Compression”. Statistical Signal Processing, SSP `07. IEEE/SP 14th Workshop, pp. 181-184, 2007.
[56] Sameni, R., C. Jutten and M.B. Shamsollahi, “Multichannel Electrocardiogram Decomposition Using Periodic Component Analysis,” Biomed. Eng., IEEE Trans., 55(8), pp. 1935-1940, 2008.
[57] Kok-Kiong, P. and P. Marziliano, 2008. “Compression of Neonatal EEG Seizure Signals with Finite Rate of Innovation”. Acoustics, Speech and Signal Processing. ICASSP 2008. IEEE International Conference, pp. 433-436, 2008.
[58] Sriraam, N. and C. Eswaran, “An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals”. Inf. Technol. Biomed., IEEE Trans., 12(5): 587-594, 2008.
[59] K. Srinivasan and M.R. Reddy, “Selection of Optimal Wavelet for Lossless EEG Compression for Real-time Applications”, Indian J. of Biomechanics, 2009, pp. 241-245.
[60] H. Garry, M.G. Brian and G. Martin, 2000. Efficient EEG Compression Using JPEG 2000 with Coefficient Thresholding. 2010, UCC, Cork.
[61] Higgins, G., S. Faul, R.P. McEvoy, B. McGinley, M. Glavin, W.P. Marnane and E. Jones, 2010a. “EEG Compression Using JPEG2000: How much loss is too much? Engineering in Medicine and Biology Society (EMBC)”, Annual IEEE Inter. Conf., pp. 614-617, 2010.
[62] B. Mijovi´c, V. Mati´c, M. De Vos, and S. V. Huffel, “Independent Component Analysis as a Preprocessing Step for Data Compression of Neonatal EEG”, Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, pp. 7316-7319, 2011.
[63] N. Sriraam, “A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors”, International J. of Telemedicine and Applications, Vol. 2012, 2012.
[64] K. Srinivasan, J. Dauwels, and M. R. Reddy,” Multichannel EEG Compression: Wavelet-Based Image and Volumetric Coding Approach”, IEEE J. of Biomedical and Health Informatics, Vo. 17, No. 1, pp. 113-120, Jan. 2013.
[65] Darshana, D. Pawarand, A. B. Nandgaonkar, “EEG Compression Using EZW Technique”, International Journal of Science, Engineering and Technology Research (IJSETR), vol. 2, Issue 7, pp. 1509-1512, July 2013.
[66] Srinivasan, K. and R.M. Ramasubba, “Efficient Preprocessing Technique for Real-time Lossless EEG Compression,” Elect. Lett, 46(1): 26-27, 2010.
[67] Higgins, G., B. McGinley, M .Glavin and E. Jones, “Low Power Compression of EEG Signals Using JPEG2000,” 4th International Conf. on Pervasive Computing Technologies for Healthcare, pp. 1-4, 2010.
[68] A. Katharotiya, S. Patel, and M. Goyani, “Comparative Analysis between DCT and DWT Techniques of Image Compression”, Journal of Information Engineering and Applications, Vol. 1, No.2, 2011.

Keywords
Medical image compression, Brain image compression, EEG, CT, Run length encoding, Huffman encoding, LZW, DCT, DWT