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Medical image encryption and compression by adaptive sigma filterized synorr certificateless signcryptive Levenshtein entropy-coding-based deep neural learning

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Abstract

Pre-processing of medical images plays a vibrant part in the field of medicine to detect patient’s disease at an earlier stage. Hospitals and medical centers generate an enormous volume of digital medical images day by day, which is used for several purposes of diagnostic procedures. Because of many images,for secured transmission, image compression is required  to reduce the redundancies in the image and to accomplish the proficient image communication. To reduce the redundancies in the image and to accomplish the proficient communication of images. A competent Adaptive sigma filterized synorr certificateless signcryptive Levenshtein entropy coding-based deep neural learning (ASFSCSLEC-DNL) technique is presented to develop the image encryption and compression. The main goal of the ASFSCSLEC-DNL technique was to improve the security level of medical image transmission. The deep feed-forward artificial neural network was applied in the ASFSCSLEC-DNL technique for medical image pre-processing, encryption, and compression with multiple layers. The adaptive sigma filter was employed to denoise the medical image. The medical image encryption and signature generation were done with synorr certificateless signcryption. Finally, Levenshtein entropy encoding was applied to compress images. Then the compressed image was sent to the receiver where the decompression and decryption are implemented using Levenshtein entropy decoding and synorr certificateless decryption. Investigational estimation was carried out in chest X-ray medical images and the results of ASFSCSLEC-DNL technique proved more capable in terms of higher peak signal to noise ratio and compression ratio with lesser encryption time compared to the existing state-of-the-art methods.

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Abbreviations

ASFSCSLEC-DNL:

Adaptive sigma filterized synorr certificateless signcryptive Levenshtein entropy coding-based deep neural learning

BP:

Back propagation

PSNR:

Peak signal to noise ratio

RSA:

Rivest–Shamir–Adleman

DCT:

Discrete cosine transform

DNA:

Deoxyribo nucleic acid

NL4DLM-DNA:

Non-linear 4D logistic map and DNA

DRPE:

Double random phase encoding

PWLCM:

Piece-wise linear chaotic map

RDH:

Reversible data hiding

ROI:

Region of interest

ID:

Identity

MSE:

Mean square error

KB:

Kilo-bytes

dB:

Decibels

References

  1. Yang, F., Mou, J., Sun, K., Chu, R.: Lossless image compression-encryption algorithm based on BP neural network and chaotic system, multimedia tools and applications, pp. 1–30. Springer, Berlin (2020)

    Google Scholar 

  2. AnkitaVaish, SonamGautam, Kumar, M.: A wavelet based approach for simultaneous compression and encryption of fused images. J. King Saud Univ. Comput. Inf. Sci. Elsevier 31(2), 208–217 (2019)

    Google Scholar 

  3. QiuaGonga, K.L., Deng, C., Zhou, N.: An optical image compression and encryption scheme based on compressive sensing and RSA algorithm. Opt. Lasers Eng. Elsevier 121, 169–180 (2019)

    Article  Google Scholar 

  4. Hao, Z., Xiao-qing, W., Yu-jie, S., Xing-yuan, W.: A novel method for lossless image compression and encryption based on LWT. SPIHT Cell. Autom. Signal Process. Image Commun. Elsevier 84, 1–17 (2020)

    Google Scholar 

  5. Abdmouleh, M.K., Khalfallaha, A., Bouhlel, M.S.: A novel selective encryption scheme for medical images transmission based-on JPEG compression algorithm. Proced. Comput. Sci. Elsevier 112, 369–376 (2017)

    Article  Google Scholar 

  6. Junxin Chen, Yu., Zhang, L.Q., Chong, Fu., Lisheng, Xu.: Exploiting chaos-based compressed sensing and cryptographic algorithm for image encryption and compression. Opt. Laser Technol. Elsevier 99, 238–248 (2018)

    Article  Google Scholar 

  7. Stalin, S., Maheshwary, P., Shukla, P.K., Maheshwari, M., Gour, B., Khare, A.: Fast and secure medical image encryption based on non linear 4D logistic map and DNA sequences (NL4DLM_DNA). J. Med. Syst. Springer 43, 1–17 (2019)

    Article  Google Scholar 

  8. Sengupta, A., Rathor, M.: Structural obfuscation and crypto-steganography-based secured JPEG compression hardware for medical imaging systems. IEEE Access. 8, 6543–6565 (2020)

    Article  Google Scholar 

  9. XinshengLi, T.L., Jiang, Wu., ZhilongXie, J.S.: Joint image compression and encryption based on sparse Bayesian learning and bit-level 3D Arnold cat maps. PLoS ONE 14(12), 1–23 (2019)

    Google Scholar 

  10. Khan, M., Waseem, H.M.: A novel image encryption scheme based on quantum dynamical spinning and rotations. PLoS ONE 13(11), 1–23 (2020)

    Google Scholar 

  11. Chen, M., Ma, G., Tang, C., Lei, Z.: Generalized optical encryption framework based on Shearlets for medical image. Opt. Lasers Eng. Elsevier 128, 1–10 (2020)

    Google Scholar 

  12. Sasikaladevi, N., Geetha, K., Sriharshini, K., Durga Aruna, M.: H3-hybrid multilayered hyper chaotic hyper elliptic curve based image encryption system. Opt. Laser Technol. Elsevier 127, 1–10 (2020)

    Google Scholar 

  13. Raja, S.P.: Joint medical image compression–encryption in the cloud using multiscale transform-based image compression encoding techniques. Sādhanā Springer 44, 1–10 (2019)

    Article  Google Scholar 

  14. Wang, H., Xiao, Di., Li, M., Xiang, Y., Li, X.: A visually secure image encryption scheme based on parallel compressive sensing. Signal Process. Elsevier 155, 218–232 (2019)

    Article  Google Scholar 

  15. Abd El-Latif, A.A., Abd-El-Atty, B., Talha, M.: Robust encryption of quantum medical images. IEEE Access 6, 1073–1081 (2017)

    Article  Google Scholar 

  16. Ali Hajjaji, M., Dridi, M., Mtibaa, A.: A medical image crypto-compression algorithm based on neural network and PWLCM. Multimed. Tools Appl. Springer 78(11), 1–18 (2019)

    Google Scholar 

  17. Haddad, S., Coatrieux, G., Cozic, M., Bouslimi, D.: Joint watermarking and lossless JPEG-LS compression for medical image security. IRBM Elsevier 38(4), 198–206 (2017)

    Article  Google Scholar 

  18. Yang, Y., Xiao, X., Cai, X., Zhang, W.: A secure and privacy-preserving technique based on contrast-enhancement reversible data hiding and plaintext encryption for medical images. IEEE Signal Process. Lett. 27, 256–260 (2020)

    Article  Google Scholar 

  19. Belazi, A., Talha, M., Kharbech, S., Xiang, W.: Novel medical image encryption scheme based on chaos and DNA encoding. IEEE Access 7, 36667–36681 (2019)

    Article  Google Scholar 

  20. Yang, Y., Zhang, W., Liang, D., Nenghai, Yu.: A ROI-based high capacity reversible data hiding scheme with contrast enhancement for medical images. Multimed. Tools Appl. Springer 77, 18043–18065 (2018)

    Article  Google Scholar 

  21. Yan, C., Gong, B., Wei, Y., Gao, Y.: Deep multi-view enhancement hashing for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 14(8), 1–8 (2020)

    Google Scholar 

  22. Yan, C., Shao, B., Zhao, H., Ning, R., Zhang, Y., Feng, Xu.: 3D room layout estimation from a single RGB image. IEEE Trans. Multimed. 22(11), 3014–3024 (2020)

    Article  Google Scholar 

  23. Yan, C., Li, Z., Zhang, Y., Liu, Y., Ji, X., Zhang, Y.: Depth image denoising using nuclear norm and learning graph model. ACM Trans. Multimed. Comput. Commun. Appl. 16(4), 1 (2020)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of ECE, Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, India

    C. Thirumarai Selvi

  2. Department of EEE, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India

    J. Amudha

  3. Department of ECE, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India

    R. Sudhakar

Authors
  1. C. Thirumarai Selvi
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  2. J. Amudha
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  3. R. Sudhakar
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Correspondence to C. Thirumarai Selvi.

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Communicated by Y. Zhang.

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A research work is carried out to secure medical image transmission. A novel adaptive sigma filterized synorr certificateless signcryptive Levenshtein entropy coding-based deep neural learning (ASFSCSLEC-DNL) technique is developed. We used the deep feed-forward artificial neural network for medical image encryption and compression on the sender side. A multi-layer percepted deep learning-based image decompression and decryption are carried out at the receiver side to reconstruct the original image.

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Selvi, C.T., Amudha, J. & Sudhakar, R. Medical image encryption and compression by adaptive sigma filterized synorr certificateless signcryptive Levenshtein entropy-coding-based deep neural learning. Multimedia Systems 27, 1059–1074 (2021). https://doi.org/10.1007/s00530-021-00764-y

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  • DOI: https://doi.org/10.1007/s00530-021-00764-y

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