Mathematical analysis of wavelet-based multi-image compression in medical diagnostics

Gowda, V. Dankan; Sunitha, Y. N.; Chakrasali, Sadashiva V.; Prasad, K.D.V.; Sarma, Parismita; Rahman, Mirzanur

doi:10.47974/JDMSC-1912

Open Access Research Article

Mathematical analysis of wavelet-based multi-image compression in medical diagnostics

^*V. Dankan GowdaCorresponding authordankan.v@bmsit.inDepartment of Electronics and Communication Engineering BMS Institute of Technology and ManagementBangalore, Karnataka, 560119, IndiaView full profile → , Y. N. Sunithasunithayn@sjbit.edu.inDepartment of Electronics and Communication Engineering SJB Institute of Technology No 67, BGS Health and Education City Dr. Vishnuvardhana Road, KengeriBangalore, Karnataka, IndiaView full profile → , Sadashiva V. Chakrasalisadashiva.c@msrit.eduDepartment of Electronics and Communication Engineering M S Ramaiah Institute of Technology MSR Nagar, MSRIT PostDepartment of Electronics and Communication Engineering M S Ramaiah Institute of Technology MSR Nagar MSRIT PostBengaluru, Karnataka, IndiaView full profile → , K.D.V. Prasadkdv.prasad@sibmhyd.edu.inDepartment of Research Symbiosis Institute of Business Management; Department of Research Symbiosis International (Deemed University)Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Hyderabad, Telangana, 509217, IndiaView full profile → , Parismita Sarmapari@gauhati.ac.inDepartment of Information Technology Gauhati UniversityGuwahati, Assam, IndiaView full profile → , Mirzanur Rahmanmr@gauhati.ac.inDepartment of Information Technology Gauhati UniversityGuwahati, Assam, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 675–687Vol. 27Issue 2-BMarch 2024DOI: 10.47974/JDMSC-1912 Crossmark XML

Published Online:: 11 Apr 2024
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-1912
Pages:: 675–687

Abstract

Accurate diagnosis and well-informed treatment choices are only possible with the help of sophisticated medical imaging technology. The introduction of digital imaging methods has led to a dramatic rise in the volume of data associated with medical images, which in turn has led to difficulties in their storage, transmission, and administration. It is crucial to utilize effective image compression techniques to address these challenges without compromising the diagnostic integrity of the pictures. The wavelet transform has matured into a potent method for striking a good compromise between picture quality and file size reduction while compressing. The safe and efficient transfer of medical image data is a major concern in today’s healthcare settings. In this academic investigation, we investigate how wavelet transform-based techniques may be used to enhance medical picture compression. The proposed techniques optimize compression ratios while maintaining diagnostic picture quality by making use of the wavelet transform’s multi-resolution and frequency localization features. To address the unique challenges given by medical image collections, various iterations of the wavelet transform and compression techniques are investigated. Through a series of detailed tests involving several medical picture modalities, the effectiveness of these technologies is thoroughly evaluated, demonstrating their effectiveness in achieving significant data reduction without sacrificing clinical information.

Keywords

Medical imaging Image compression Wavelet transform Efficient compression Multiple images Data reduction Healthcare applications

Subject Classifications

Primary 93A30Secondary 49K15

References

[1] R. Krishnaswamy and S. NirmalaDevi, “Efficient medical image compression based on integer wavelet transform,” 2020 Sixth International Conference on Bio Signals, Images, and Instrumentation (ICBSII), Chennai, India, pp. 1-5 (2020), doi: 10.1109/ICBSII49132.2020.9167597.
[2] M. R. Yelland and F. Aghdasi, “Wavelet transform for medical image compression,” 1999 IEEE Africon. 5th Africon Conference in Africa (Cat. No.99CH36342), Cape Town, South Africa, pp. 303-308 vol. 1 (1999), doi: 10.1109/AFRCON.1999.820829.
[3] J. Wang and K. Huang, “Medical image compression by using three-dimensional wavelet transformation,” in IEEE Transactions on Medical Imaging, vol. 15, no. 4, pp. 547-554, Aug. (1996), doi: 10.1109/42.511757.
[4] Lee Sung, Kyu Sung Gyun Kim and Young Hwan Kim, “Multimode Image Compression Algorithm Employing Multiple-Choice Knapsack Problem-Based Encoding Mode Selection”, Journal of Display Technology, vol. 12.6, pp. 549-556 (2016).
[5] Sun Chang and En-Hui Yang, “An efficient DCT-based image compression system based on Laplacian transparent composite model”, IEEE Transactions on Image Processing, vol. 24.3, pp. 886-900 (2015).
[6] Shi Cuiping, Junping Zhang and Ye Zhang, “A Novel Vision-Based Adaptive Scanning for the Compression of Remote Sensing Images”, IEEE Transactions on Geoscience and Remote Sensing, vol. 54.3, pp. 1336-1348 (2016).
[7] M. A. Shaik Mydin, M. H. Alkawaz, K. Z. Ghafoor, O. F. Mohammad and M. G. Md Johar, “A Study on Medical Image Compression Techniques based on Huffman Coding and Discrete Wavelet Transform (DWT),” 2021 IEEE 9th Conference on Systems, Process and Control (ICSPC 2021), Malacca, Malaysia, pp. 86-91 (2021), doi: 10.1109/ICSPC53359.2021.9689166.
[8] D.D. Panda and D. Swain, “Competency assessment of image compression in the lossy and lossless domain”, Proceedings of the 4th International Conference on Image Information Processing (ICIIP’1 7), pp. 1-4, December 21–23 (2017), ISBN 978-1-5090-6735-0.
[9] Y. A. Salih, A. Ali Mohammed and L. Edwar George, “Improved Image Compression Scheme Using Hybrid Encoding Algorithm”, Kurdistan J. Appl. Res., vol. 4, no. 2, pp. 90-101 (2019).
[10] M. A. Hussin, F. A. Poad and A. Joret, “A Comparative Study on Improvement of Image Compression Method using Hybrid DCT-DWT Techniques with Huffman Encoding for Wireless Sensor Network Application”, Int. J. Integr. Eng., vol. 11, no. 3, pp. 149-158 (2019).
[11] K. Gupta, R.L. Verma and Md. Sanawer Alam, “Lossless Medical Image Compression Using Predictive Coding and Integer Wavele Transform based on Minimum Entropy Criteriat”, International Journal of Application or Innovation in Engineering & Management (IJAIEM), vol. 2, pp. 98-106 (2018).
[12] Neelima, N., Sekhar, J. N. Chandra, Pullagura, Lokaiah, Sri, P. S. G. Aruna, Saini, Dilip Kumar Jang Bahadur & Pande, Sandeep Dwarkanath. Development in intelligent autonomous agents for the high-level cognitive functions like reasoning, planning, learning and abstraction, Journal of Interdisciplinary Mathematics, 26:3, 301-310 (2023), DOI: 10.47974/JIM-1661.
[13] Avinash, Kumaraswamy, S., Sarma, Parismita , Hussain, Naziya, Dixit, Santosh Kumar & Gupta, Anand Kumar. A novel approach of unsupervised feature selection using iterative shrinking and expansion algorithm, Journal of Interdisciplinary Mathematics, 26:3, 519-530 (2023), DOI: 10.47974/JIM-1678.
[14] Gite, Pratik, Premkumar, S., Hussain, Naziya & Chinamuttevi, Veera Sivakumar. A novel RF-SMOTE model to enhance the definite apprehensions for IoT security attacks, Journal of Discrete Mathematical Sciences and Cryptography, 26:3, 861-873 (2023), DOI: 10.47974/JDMSC-1766.
[15] Pullela SVVSR, Latha, J., Selvakumar, C. , Shekhar, R. & Chaturvedi, Abhay. Securing networked image transmission using public-key cryptography and identity authentication, Journal of Discrete Mathematical Sciences and Cryptography, 26:3, 779-791 (2023), DOI: 10.47974/JDMSC-1754.
[16] M. H. Alkawaz, M. Al Tamil Veeran, A. I. Hajamydeen and O. I. Al-Sanjary, “An overview of Advanced Optical Flow Techniques for Copy Move Video Forgery Detection”, 2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), pp. 319-324 (2021).
[17] Avinash Sharma, M. Nagabushanam, H. G. Govardhana Reddy & K. Raghavendra. Vector space modelling-based intelligent binary image encryption for secure communication, Journal of Discrete Mathematical Sciences and Cryptography, 25:4, 1157-1171 (2022), DOI: 10.1080/09720529.2022.2075090.
[18] D. Ravichandran, R. Nimmatoori and M. R. A. Dhivakar, “Performance of wavelet based image compression on medical images for cloud computing,” 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, pp. 297-302 (2016).
[19] U. Patbhaje, R. Kumar, A. Kumar and H. . -N. Lee, “Compression of medical image using wavelet based sparsification and coding,” 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, pp. 394-398 (2017), doi: 10.1109/SPIN.2017.8049981.
[20] M. Kumar and A. Vaish, “An efficient encryption-then-compression technique for encrypted images using SVD”, Digit. Signal Process., vol. 60, pp. 81-89 (2017).

Views: 219Downloads: 11Citations: 5