Skip to main content

Advertisement

SpringerLink
Log in

Medical Image Encryption using Biometric Image Texture Fusion

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

In conjunction with pandemics, medical image data are growing exponentially. In some countries, hospitals collect biometric data from patients, such as fingerprints, iris, or faces. This data can be used for things like identity verification and security management. However, this medical data can be easily compromised by hackers. In order to prevent illegal tampering with medical images and invasion of privacy, a new texture fusion medical image encryption (TFMIE) algorithm derived from biometric images is proposed, which can encrypt the image using biometric information for storage or transmission. First, the medical image is decomposed into n-bit-planes by bit-plane decomposition. Secondly, a fusion image is generated by a biometric image with a circular local binary pattern and pixel-weighted average method. The fused image is further decomposed into n bit-planes through bit-plane decomposition and performs XOR operation with the original medical image in reverse order. Following the execution of the XOR operation, a new scrambling and diffusion algorithm based on a one-dimensional fractional trigonometric function (1DFTF) chaotic map is employed to form the cipher image. The experimental results show that compared with the existing methods, the average information entropy value of TFMIE is 7.99, and the average values of NPCR and UACI reach 0.9958 and 0.3346, respectively, which have strong key sensitivity, good robustness, and anti-attack ability. The method is lossless and has high transmission efficiency, which can meet the needs of medical big data encryption.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Prabhu, Pavithra, and K. N. Manjunath. "Secured transmission of medical images in radiology using aes technique." Computer Aided Intervention and Diagnostics in Clinical and Medical Images. Springer, Cham, 2019. 103–112.

  2. Rajesh, Chilukamari, and Sushil Kumar. "An evolutionary block based network for medical image denoising using Differential Evolution." Applied Soft Computing 121 (2022): 108776.

    Article  Google Scholar 

  3. Hatamizadeh, Ali, et al. "Unetr: Transformers for 3d medical image segmentation." Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2022.

  4. Dimililer, Kamil. "DCT-based medical image compression using machine learning." Signal, Image and Video Processing 16.1 (2022): 55-62.

    Article  Google Scholar 

  5. Suganyadevi, S., Seethalakshmi, V. & Balasamy, K. A review on deep learning in medical image analysis. Int J Multimed Info Retr (2021). https://doi.org/10.1007/s13735-021-00218-1

  6. Suganyadevi, S., Seethalakshmi, V. CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network. Wireless Pers Commun (2022). https://doi.org/10.1007/s11277-022-09864-y

    Article  Google Scholar 

  7. Singh, Satendra Pal, and Gaurav Bhatnagar. "A novel biometric inspired robust security framework for medical images." IEEE Transactions on Knowledge and Data Engineering 33.3 (2019): 810–823.

  8. Lakshmi, C., et al. "Encryption and watermark-treated medical image against hacking disease—An immune convention in spatial and frequency domains." Computer Methods and Programs in Biomedicine 159 (2018): 11-21.

    Article  CAS  PubMed  Google Scholar 

  9. Balasamy K, Suganyadevi S (2021) “A fuzzy based ROI selection for encryption and watermarking in medical image using DWT and SVD” Multimed Tools Appl 80, 7167–7186. https://doi.org/10.1007/s11042-020-09981-5.

    Article  Google Scholar 

  10. Balasamy, K., Krishnaraj, N. & Vijayalakshmi, K. Improving the security of medical image through neuro-fuzzy based ROI selection for reliable transmission. Multimed Tools Appl 81, 14321–14337 (2022). https://doi.org/10.1007/s11042-022-12367-4.

    Article  Google Scholar 

  11. Balasamy, K., Krishnaraj, N. & Vijayalakshmi, K. An Adaptive Neuro-Fuzzy Based Region Selection and Authenticating Medical Image Through Watermarking for Secure Communication. Wireless Pers Commun (2021). https://doi.org/10.1007/s11277-021-09031-9.

    Article  Google Scholar 

  12. Abdelfatah R I, Saqr H M, Nasr M E. An efficient medical image encryption scheme for (WBAN) based on adaptive DNA and modern multi chaotic map[J]. Multimedia Tools and Applications, 2023, 82(14): 22213-22227.

    Article  Google Scholar 

  13. Zhang, Bin, et al. "A plain-image correlative semi-selective medical image encryption algorithm using enhanced 2D-logistic map." Multimedia Tools and Applications (2022): 1–28.

  14. Lai Q, Hu G, Erkan U, et al. High-efficiency medical image encryption method based on 2D Logistic-Gaussian hyperchaotic map[J]. Applied Mathematics and Computation, 2023, 442: 127738.

    Article  Google Scholar 

  15. Ferdush, Jannatul, Mahbuba Begum, and Mohammad Shorif Uddin. "Chaotic lightweight cryptosystem for image encryption." Advances in Multimedia 2021 (2021).

  16. Priya, S., and B. Santhi. "A novel visual medical image encryption for secure transmission of authenticated watermarked medical images." Mobile networks and applications (2019): 1–8.

  17. Pavithra, V., and C. Jeyamala. "A survey on the techniques of medical image encryption." 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2018.

  18. Manikandan, Veerappan, and Rengarajan Amirtharajan. "A simple embed over encryption scheme for DICOM images using Bülban Map." Medical & Biological Engineering & Computing 60.3 (2022): 701-717.

    Article  Google Scholar 

  19. Carey, Alycia N., and Justin Zhan. "A cancelable multi-modal biometric based encryption scheme for medical images." 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020.

  20. Lin, Hsin-Chih, Ling-Ling Wang, and Shi-Nine Yang. "Extracting periodicity of a regular texture based on autocorrelation functions." Pattern recognition letters 18.5 (1997): 433-443.

    Article  Google Scholar 

  21. Zhang, Xin, et al. "A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm." Sensors 17.7 (2017): 1474.

  22. Li, Gang, and Youchuan Wan. "Adaptive seeded region growing for image segmentation based on edge detection, texture extraction and cloud model." International Conference on Information Computing and Applications. Springer, Berlin, Heidelberg, 2010.

  23. Ahmadian, A., and A. Mostafa. "An efficient texture classification algorithm using Gabor wavelet." Proceedings of the 25th annual international conference of the IEEE engineering in medicine and biology society (IEEE Cat. No. 03CH37439). Vol. 1. IEEE, 2003.

  24. Ojala, Timo, Matti Pietikäinen, and Topi Mäenpää. "A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification." International conference on advances in pattern recognition. Springer, Berlin, Heidelberg, 2001.

  25. Wang C H, Li L H. Image fusion at pixel level algorithm is introduced and the evaluation criteria[C]//2010 International Conference on Educational and Network Technology. IEEE, 2010: 585–588.

  26. Zhao, Yue, and Lingfeng Liu. "A Bit Shift Image Encryption Algorithm Based on Double Chaotic Systems." Entropy 23.9 (2021): 1127.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wang, Xingyuan, Cheng Liu, and Donghua Jiang. "Visually meaningful image encryption scheme based on new-designed chaotic map and random scrambling diffusion strategy." Chaos, Solitons & Fractals 164 (2022): 112625.

    Article  Google Scholar 

  28. Tang, Zhenjun, et al. "Image encryption with double spiral scans and chaotic maps." Security and Communication Networks 2019 (2019).

  29. MedPix, Free Online Medical Image Database, available at: https://medpix.nlm.nih.gov/home

  30. Roy, Mousomi, Shouvik Chakraborty, and Kalyani Mali. "The MSK: a simple and robust image encryption method." Multimedia Tools and Applications 80.14 (2021): 21261-21291.

    Article  Google Scholar 

  31. Farah, M. A., A. Farah, and T. Farah. "An image encryption scheme based on a new hybrid chaotic map and optimized substitution box." Nonlinear Dynamics 99.4 (2020): 3041-3064.

    Article  Google Scholar 

  32. Masood F, Driss M, Boulila W, et al. A lightweight chaos-based medical image encryption scheme using random shuffling and XOR operations[J]. Wireless Personal Communications, 2022, 127(2): 1405-1432.

    Article  Google Scholar 

Download references

Funding

This project is supported in part by the National Natural Science Foundation of China: 62262062, the major programs incubation plan of Xizang Minzu University: 22MDZ03, and Research Team Project for Xizang-related Network Information Content and Data Security (No.324042000709).

Author information

Authors and Affiliations

  1. School of Information Engineering, Xizang Minzu University, 712082, Xianyang, Shaanxi, China

    Zhaoyang Liu & Ru Xue

  2. Key Laboratory of Optical Information Processing and Visualization Technology of Tibet Autonomous Region, 712082, Xianyang, Shaanxi, China

    Zhaoyang Liu & Ru Xue

  3. Xizang Cyberspace Governance Research Center, 712082, Xianyang, Shaanxi, China

    Zhaoyang Liu & Ru Xue

Authors
  1. Zhaoyang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ru Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Zhaoyang Liu wrote the main manuscript text and Ru Xue prepared Figs. 1–6. All authors reviewed the manuscript.

Corresponding author

Correspondence to Ru Xue.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Z., Xue, R. Medical Image Encryption using Biometric Image Texture Fusion. J Med Syst 47, 112 (2023). https://doi.org/10.1007/s10916-023-02003-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-023-02003-5

Keywords

Search

Navigation