A Hybrid Approaches for Medical Image Compression: Survey
DOI:
https://doi.org/10.29304/jqcm.2023.15.2.1237Keywords:
Compression Ratio, Wavelet Transform, image CompressionAbstract
Data compression is necessary for the storing, transfer, and manipulation of digital data in today's rapidly changing world due to the widespread use of medical technology and the vast data creation by various medical modalities. Many scientists and engineers have proposed new compression processes, methods, and algorithms in recent years. There is a discussion of the current state of these compression strategies, including their recent advances in the form of hybrid techniques, performance requirements, legal challenges, and the potential to advance medical technology.
Downloads
Download data is not yet available.
References
1] F. Liu, M. Hernandez-Cabronero, V. Sanchez, M. W. Marcellin, and A. Bilgin, “The current role of image compression standards in medical imaging,” Information (Switzerland), vol. 8, no. 4. MDPI AG, Oct. 19, 2017. doi: 10.3390/info8040131.
[2] B. Shickel, P. J. Tighe, A. Bihorac, and P. Rashidi, “Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis,” IEEE J Biomed Health Inform, vol. 22, no. 5, pp. 1589–1604, 2017.
[3] S. Bhavani and K. Thanushkodi, “A survey on coding algorithms in medical image compression,” International Journal on Computer Science and Engineering, vol. 2, no. 5, pp. 1429–1434, 2010.
[4] S. E. Umbaugh, Digital image processing and analysis: human and computer vision applications with CVIPtools. CRC press, 2010.
[5] M. Abo-Zahhad, R. R. Gharieb, S. M. Ahmed, and M. K. Abd-Ellah, “Huffman image compression incorporating DPCM and DWT,” Journal of Signal and Information Processing, vol. 6, no. 02, p. 123, 2015.
[6] I. Daubechies, “The wavelet transform, time-frequency localization and signal analysis,” IEEE Trans Inf Theory, vol. 36, no. 5, pp. 961–1005, 1990.
[7] C. Valens, “A really friendly guide to wavelets,” 1999.
[8] R. Starosolski, “Hybrid adaptive lossless image compression based on discrete wavelet transform,” Entropy, vol. 22, no. 7, p. 751, 2020.
[9] M. M. H. Chowdhury and A. Khatun, “Image compression using discrete wavelet transform,” International Journal of Computer Science Issues (IJCSI), vol. 9, no. 4, p. 327, 2012.
[10] T. S. Patel, R. Modi, and K. J. Patel, “Image compression using DWT and vector quantization,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 1, no. 3, p. 653, 2013.
[11] A. A. Mohammed and J. A. Hussein, “Efficient hybrid transform scheme for medical image compression,” Int J Comput Appl, vol. 27, no. 7, pp. 975–8887, 2011.
[12] J. Janet, D. Mohandass, and S. Meenalosini, “Lossless compression techniques for medical images in telemedicine,” Advances in Telemedicine Technologies, Enabling Factors, Scenario, pp. 111–130, 2011.
[13] P. N. T. Ammah and E. Owusu, “Robust medical image compression based on wavelet transform and vector quantization,” Inform Med Unlocked, vol. 15, p. 100183, 2019.
[14] H. B. Kekre, P. Natu, and T. Sarode, “Color image compression using vector quantization and hybrid wavelet transform,” Procedia Comput Sci, vol. 89, pp. 778–784, 2016.
[15] R. Kumar, U. Patbhaje, and A. Kumar, “An efficient technique for image compression and quality retrieval using matrix completion,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 4, pp. 1231–1239, 2022.
[16] A. Al-Fayadh, A. J. Hussain, P. Lisboa, D. Al-Jumeily, and M. Al-Jumaily, “A hybrid image compression method and its application to medical images,” in 2009 Second International Conference on Developments in eSystems Engineering, 2009, pp. 107–112.
[17] M. F. Radad, A. Obeid, and A. Al-Fayadh, “A Hybrid Discrete Wavelet Transform with Vector Quantization for Efficient Medical Image Compression”, doi: 10.14704/nq.2022.20.8.NQ44909.
[18] A. Mofreh, T. M. Barakat, and A. M. Refaat, “A new lossless medical image compression technique using hybrid prediction model,” Signal Processing: An International Journal (SPIJ), vol. 10, no. 3, p. 20, 2016.
[19] S. S. Parikh, D. Ruiz, H. Kalva, G. Fernández-Escribano, and V. Adzic, “High bit-depth medical image compression with hevc,” IEEE J Biomed Health Inform, vol. 22, no. 2, pp. 552–560, 2017.
[20] T. Somassoundaram and N. P. Subramaniam, “High performance angiogram sequence compression using 2D bi-orthogonal multiwavelet and hybrid SPIHT-DEFLATE algorithm,” Indian J Sci Technol, vol. 8, no. 14, p. 1, 2015.
[21] B. Perumal and M. P. Rajasekaran, “A hybrid discrete wavelet transform with neural network back propagation approach for efficient medical image compression,” in 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), 2016, pp. 1–5.
[22] T. Karthikeyan and C. Thirumoorthi, “A hybrid medical image compression techniques for lung cancer,” Indian J Sci Technol, vol. 9, no. 39, 2016.
[23] D. S. Thomas, M. Moorthi, and R. Muthalagu, “Medical image compression based on automated ROI selection for telemedicine application,” Int. J. Eng. Comput. Sci, vol. 3, pp. 3638–3642, 2014.
[24] M. M. S. Rani and P. Chitra, “A hybrid medical image coding method based on haar wavelet transform and particle swarm optimization technique,” International Journal of Pure and Applied Mathematics, vol. 118, no. 8, pp. 3056–3067, 2018.
[25] A. Al-Fayadh and H. Majid, “Singular value decomposition based classified vector quantization image compression method using discrete sine transform,” ARPN J. Eng. Appl. Sci., vol. 11, no. 21, pp. 12883–12891, 2016.
[26] M. U. A. Ayoobkhan, E. Chikkannan, and K. Ramakrishnan, “Lossy image compression based on prediction error and vector quantisation,” EURASIP J Image Video Process, vol. 2017, no. 1, pp. 1–13, 2017.
[27] A. M. Rufai, G. Anbarjafari, and H. Demirel, “Lossy medical image compression using Huffman coding and singular value decomposition,” in 2013 21st Signal processing and communications applications conference (SIU), 2013, pp. 1–4.
[2] B. Shickel, P. J. Tighe, A. Bihorac, and P. Rashidi, “Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis,” IEEE J Biomed Health Inform, vol. 22, no. 5, pp. 1589–1604, 2017.
[3] S. Bhavani and K. Thanushkodi, “A survey on coding algorithms in medical image compression,” International Journal on Computer Science and Engineering, vol. 2, no. 5, pp. 1429–1434, 2010.
[4] S. E. Umbaugh, Digital image processing and analysis: human and computer vision applications with CVIPtools. CRC press, 2010.
[5] M. Abo-Zahhad, R. R. Gharieb, S. M. Ahmed, and M. K. Abd-Ellah, “Huffman image compression incorporating DPCM and DWT,” Journal of Signal and Information Processing, vol. 6, no. 02, p. 123, 2015.
[6] I. Daubechies, “The wavelet transform, time-frequency localization and signal analysis,” IEEE Trans Inf Theory, vol. 36, no. 5, pp. 961–1005, 1990.
[7] C. Valens, “A really friendly guide to wavelets,” 1999.
[8] R. Starosolski, “Hybrid adaptive lossless image compression based on discrete wavelet transform,” Entropy, vol. 22, no. 7, p. 751, 2020.
[9] M. M. H. Chowdhury and A. Khatun, “Image compression using discrete wavelet transform,” International Journal of Computer Science Issues (IJCSI), vol. 9, no. 4, p. 327, 2012.
[10] T. S. Patel, R. Modi, and K. J. Patel, “Image compression using DWT and vector quantization,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 1, no. 3, p. 653, 2013.
[11] A. A. Mohammed and J. A. Hussein, “Efficient hybrid transform scheme for medical image compression,” Int J Comput Appl, vol. 27, no. 7, pp. 975–8887, 2011.
[12] J. Janet, D. Mohandass, and S. Meenalosini, “Lossless compression techniques for medical images in telemedicine,” Advances in Telemedicine Technologies, Enabling Factors, Scenario, pp. 111–130, 2011.
[13] P. N. T. Ammah and E. Owusu, “Robust medical image compression based on wavelet transform and vector quantization,” Inform Med Unlocked, vol. 15, p. 100183, 2019.
[14] H. B. Kekre, P. Natu, and T. Sarode, “Color image compression using vector quantization and hybrid wavelet transform,” Procedia Comput Sci, vol. 89, pp. 778–784, 2016.
[15] R. Kumar, U. Patbhaje, and A. Kumar, “An efficient technique for image compression and quality retrieval using matrix completion,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 4, pp. 1231–1239, 2022.
[16] A. Al-Fayadh, A. J. Hussain, P. Lisboa, D. Al-Jumeily, and M. Al-Jumaily, “A hybrid image compression method and its application to medical images,” in 2009 Second International Conference on Developments in eSystems Engineering, 2009, pp. 107–112.
[17] M. F. Radad, A. Obeid, and A. Al-Fayadh, “A Hybrid Discrete Wavelet Transform with Vector Quantization for Efficient Medical Image Compression”, doi: 10.14704/nq.2022.20.8.NQ44909.
[18] A. Mofreh, T. M. Barakat, and A. M. Refaat, “A new lossless medical image compression technique using hybrid prediction model,” Signal Processing: An International Journal (SPIJ), vol. 10, no. 3, p. 20, 2016.
[19] S. S. Parikh, D. Ruiz, H. Kalva, G. Fernández-Escribano, and V. Adzic, “High bit-depth medical image compression with hevc,” IEEE J Biomed Health Inform, vol. 22, no. 2, pp. 552–560, 2017.
[20] T. Somassoundaram and N. P. Subramaniam, “High performance angiogram sequence compression using 2D bi-orthogonal multiwavelet and hybrid SPIHT-DEFLATE algorithm,” Indian J Sci Technol, vol. 8, no. 14, p. 1, 2015.
[21] B. Perumal and M. P. Rajasekaran, “A hybrid discrete wavelet transform with neural network back propagation approach for efficient medical image compression,” in 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), 2016, pp. 1–5.
[22] T. Karthikeyan and C. Thirumoorthi, “A hybrid medical image compression techniques for lung cancer,” Indian J Sci Technol, vol. 9, no. 39, 2016.
[23] D. S. Thomas, M. Moorthi, and R. Muthalagu, “Medical image compression based on automated ROI selection for telemedicine application,” Int. J. Eng. Comput. Sci, vol. 3, pp. 3638–3642, 2014.
[24] M. M. S. Rani and P. Chitra, “A hybrid medical image coding method based on haar wavelet transform and particle swarm optimization technique,” International Journal of Pure and Applied Mathematics, vol. 118, no. 8, pp. 3056–3067, 2018.
[25] A. Al-Fayadh and H. Majid, “Singular value decomposition based classified vector quantization image compression method using discrete sine transform,” ARPN J. Eng. Appl. Sci., vol. 11, no. 21, pp. 12883–12891, 2016.
[26] M. U. A. Ayoobkhan, E. Chikkannan, and K. Ramakrishnan, “Lossy image compression based on prediction error and vector quantisation,” EURASIP J Image Video Process, vol. 2017, no. 1, pp. 1–13, 2017.
[27] A. M. Rufai, G. Anbarjafari, and H. Demirel, “Lossy medical image compression using Huffman coding and singular value decomposition,” in 2013 21st Signal processing and communications applications conference (SIU), 2013, pp. 1–4.
Downloads
Published
2023-09-24
How to Cite
Adnan, H., Obeid, A., & Al-Fayadh, A. (2023). A Hybrid Approaches for Medical Image Compression: Survey. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(2), Comp Page 127–135. https://doi.org/10.29304/jqcm.2023.15.2.1237
Issue
Section
Computer Articles
