Submit Your Paper
  1. Home
  2. Journals
  3. Journal of Cybersecurity and Information Management

Journal of Cybersecurity and Information Management

Journal DOI

https://doi.org/10.54216/JCIM

Submit Your Paper

2690-6775ISSN (Online) 2769-7851ISSN (Print)
Submit Your Paper

Journal Menu

Homepage Journal Overview Editorial Board Submitting Articles Indexing & Abstracting

Journal Volumes

Journal of Cybersecurity and Information Management

Volume 16 , Issue 2 , PP: 01-12, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

Performance Comparison of Wavelet Transforms based Medical Image Compression

V. Anusuya 1 * , Stency V. S. 2 * , G. Srividhya 3 , M. K. Mohammed Faizel 4 , G. Arul Kumaran 5 , R. Santhosh 6 , P. Sherubha 7

  • 1 Associate Professor, Department of IT, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India - (pgkrishanu@gmail.com)
  • 2 Assistant Professor, Department of Computer Applications, Mercy College, Palakkad, Kerala, India - (stencyvs@mercycollege.edu.in; )
  • 3 Assistant Professor (Grade I), Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamalle, Chennai, Tamil Nadu, India - (srividhyakrishiv@gmail.com)
  • 4 Assistant Professor, Department of Artificial Intelligence and Data Science, K. Ramakrishna College of Engineering, Samayapuram, Tamil Nadu, India - (moh5zal@gmail.com)
  • 5 Associate Professor, School of Computing and Information Technology, REVA University, Bangalore, Karnataka, India - (erarulkumaran@gmail.com)
  • 6 Professor, Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Tamil Nadu, India - (santhoshrd@gmail.com)
  • 7 Associate Professor, Department of Information Technology, Karpagam College of Engineering, Coimbatore, India - (sherubha.p@kce.ac.in)

    • Doi: https://doi.org/10.54216/JCIM.160201

    Received: January 03, 2025 Revised: February 17, 2025 Accepted: March 14, 2025
    Abstract

    Medical image analysis plays a vital role in diagnosis of diseases and the need of the day is to arrive at a simple and efficient compression technique. This paper proposes a comparative analysis of three different wavelet based medical image compression techniques. First algorithm is based on Bi-orthogonal wavelet with Parallel coding  (BiWT-PC) , second is based on Haar wavelet with block coding  (HWT-BC) and third algorithm is based on stationary wavelet transform with Parallel coding (SWT-PC). In this work, 3D medical image is converted into 2D slices and preprocessed using lifting scheme. Wavelet transform is applied to this preprocessed image, which divides the image into multilevel sub-bands. Then, the suitable encoding method is applied to get the compressed image. At the receiver side, the original image is recovered back by applying inverse wavelet transform and proper decoding over the compressed image. Experimentations are carried out over MRI and CT images with four quantitative metrics such as PSNR, CR, DcT and EcT. From the experimental analysis, it is observed that SWT-PC method is quite efficient since it has high PSNR and low CR.

    Keywords :

    Stationary Wavelet Transform , Haar Wavelet Transform , Biorthogonal Wavelet Transform , MRI , CT

    References

    [1]       S. Shehanaz, E. Daniel, S. R. Guntur, and S. Satrasupalli, “Optimum weighted multimodal medical image fusion using particle swarm optimization,” Sensors, vol. 231, 2021, Art. no. 166413. doi: 10.1016/j.ijleo.2021.166413.

    [2]      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.

    [3]      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. 2, pp. 123–135, 2015.

    [4]     D. Venugopal, S. Mohan, and S. Sivanantha Raja, “An efficient block-based lossless compression of medical images,” Scientific Reports, vol. 127, no. 2, pp. 754–758, 2016. doi: 10.1016/j.ijleo.2015.10.154.

    [5]     P. Selvam, S. Balachandran, S. P. Iyer, and R. Jayabal, “Hybrid transform-based reversible watermarking technique for medical images in telemedicine applications,” Personal Computing, vol. 145, pp. 655–671, 2017. doi: 10.1016/j.ijleo.2017.07.060.

    [6] R. Bagaria, S. Wadhwani, and A. K. Wadhwani, “A wavelet transform and neural network based segmentation & classification system for bone fracture detection,” Journal of Information Technology, vol. 236, 2021, Art. no. 166687. doi: 10.1016/j.ijleo.2021.166687.

    [7]    M. A. Gungor and K. Gencol, “Developing a compression procedure based on the wavelet denoising and JPEG2000 compression,” Journal of Information Technology, vol. 218, 2020, Art. no. 164933. doi: 10.1016/j.ijleo.2020.164933.

    [8]     J. Sujitha, E. B. Rajsingh, and K. Ezra, “A novel medical image compression using Ripplet transform,” Journal of Real-Time Image Processing, vol. 11, no. 2, pp. 401–412, 2016.

    [9]       H. R. Choi, S.-H. Kang, S. Lee, D.-K. Han, and Y. Lee, “Comparison of image performance for three compression methods based on digital X-ray system: Phantom study,” Networks, vol. 157, pp. 197–202, 2018. doi: 10.1016/j.ijleo.2017.11.069.

    [10]   M. Cyriac and C. Chellamuthu, “A novel visually lossless spatial domain approach for medical image compression,” European Journal of Scientific Research, vol. 71, no. 3, pp. 347–351, 2012.

    [11]   T. Brahimi, L. Boubchir, R. Fournier, and A. Naït-Ali, “An improved multimodal signal-image compression scheme with application to natural images and biomedical data,” Multimedia Tools and Applications, vol. 76, no. 15, pp. 16783–16805, 2017.

    [12]   A. Arif, A. S. Mansor, R. Logeswaran, and H. A. Karim, “Auto-shape lossless compression of pharynx and esophagus fluoroscopic images,” Journal of Medical Systems, vol. 39, no. 2, p. 5, 2015.

    [13]   Z. Zuo, X. Lan, L. Deng, S. Yao, and X. Wang, “An improved medical image compression technique with lossless region of interest,” Multimedia, vol. 126, no. 21, pp. 2825–2831, 2015. doi: 10.1016/j.ijleo.2015.07.005.

    [14]   S. Das and M. K. Kundu, “Effective management of medical information through ROI-lossless fragile image watermarking technique,” Computer Methods and Programs in Biomedicine, vol. 111, no. 3, pp. 662–675, 2013.

    [15]   J. Li, “An improved wavelet image lossless compression algorithm,” International Journal of Soft Computing, vol. 124, no. 11, pp. 1041–1044, 2013. doi: 10.1016/j.ijleo.2013.01.012.

    [16]   L. F. R. Lucas, N. M. M. Rodrigues, L. A. da Silva Cruz, and S. M. M. de Faria, “Lossless compression of medical images using 3-D predictors,” IEEE Transactions on Medical Imaging, vol. 36, no. 11, pp. 2250–2260, 2017.

    [17]   D. Spelic and B. Zalik, “Lossless compression of threshold-segmented medical images,” Journal of Medical Systems, vol. 36, no. 4, pp. 2349–2357, 2012.

    [18]   V. S. Raghavan and G. Kavitha, “Lossless compression on MRI images using SWT,” Journal of Digital Imaging, vol. 27, no. 5, pp. 594–600, 2014.

    [19]   B. Xiao, G. Lu, Y. Zhang, W. Li, and G. Wang, “Lossless image compression based on integer discrete Tchebichef transform,” Neurocomputing, vol. 214, pp. 587–593, 2016.

    [20]   V. Sanchez, R. Abugharbieh, and P. Nasiopoulos, “Symmetry-based scalable lossless compression of 3-D medical image data,” IEEE Transactions on Medical Imaging, vol. 28, no. 7, pp. 1062–1072, 2009.

    [21]   E. Ghrare and S. M. Shareef, “Quality evaluation of compressed MRI medical images for telemedicine applications,” International Journal of Biomedical and Biological Engineering, vol. 6, no. 12, pp. 641–643, 2012.

    [22]   I. Bouklihacene, M. Beladghem, and A. Bessard, “Lossy compression color medical image using CDF wavelet lifting scheme,” International Journal of Image, Graphics, and Signal Processing, vol. 5, no. 11, pp. 53–60, 2013.

    [23]   H. H. Maria, A. M. Jossy, G. Malarvizhi, and A. Jenitta, “Analysis of lifting scheme based double density dual-tree complex wavelet transform for de-noising medical images,” Optik, vol. 241, 2021, Art. no. 166883. doi: 10.1016/j.ijleo.2021.166883.

     

     

    Cite This Article As :
    Anusuya, V.. , V., Stency. , Srividhya, G.. , K., M.. , Arul, G.. , Santhosh, R.. , Sherubha, P.. Performance Comparison of Wavelet Transforms based Medical Image Compression. Journal of Cybersecurity and Information Management, vol. , no. , 2025, pp. 01-12. DOI: https://doi.org/10.54216/JCIM.160201
    Anusuya, V. V., S. Srividhya, G. K., M. Arul, G. Santhosh, R. Sherubha, P. (2025). Performance Comparison of Wavelet Transforms based Medical Image Compression. Journal of Cybersecurity and Information Management, (), 01-12. DOI: https://doi.org/10.54216/JCIM.160201
    Anusuya, V.. V., Stency. Srividhya, G.. K., M.. Arul, G.. Santhosh, R.. Sherubha, P.. Performance Comparison of Wavelet Transforms based Medical Image Compression. Journal of Cybersecurity and Information Management , no. (2025): 01-12. DOI: https://doi.org/10.54216/JCIM.160201
    Anusuya, V. , V., S. , Srividhya, G. , K., M. , Arul, G. , Santhosh, R. , Sherubha, P. (2025) . Performance Comparison of Wavelet Transforms based Medical Image Compression. Journal of Cybersecurity and Information Management , () , 01-12 . DOI: https://doi.org/10.54216/JCIM.160201
    Anusuya V. , V. S. , Srividhya G. , K. M. , Arul G. , Santhosh R. , Sherubha P. [2025]. Performance Comparison of Wavelet Transforms based Medical Image Compression. Journal of Cybersecurity and Information Management. (): 01-12. DOI: https://doi.org/10.54216/JCIM.160201
    Anusuya, V. V., S. Srividhya, G. K., M. Arul, G. Santhosh, R. Sherubha, P. "Performance Comparison of Wavelet Transforms based Medical Image Compression," Journal of Cybersecurity and Information Management, vol. , no. , pp. 01-12, 2025. DOI: https://doi.org/10.54216/JCIM.160201

    Article Statistics

    View
    163
    Download
    118

    Download