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Color medical image cryptography technology based on segmentation and fractional-order hyperchaotic system

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Abstract

This paper proposes a novel color medical image encryption method based on mean shift algorithm and fractional-order hyperchaotic system. Firstly, color medical images were divided into lesion area, detection area, and edge area by using mean shift clustering. Low-value pixels and zero-value pixels in the edge region are abandoned to improve the encryption efficiency. Secondly, the doctor-patient information is embedded into the pixel matrix of the lesion region by using discrete wavelet transform and singular value decomposition. Thirdly, the pixels of the lesion region and the detection region are processed by using fractional-order hyperchaotic system. Finally, the color medical image embedded with doctor-patient information can be quickly encrypted. Security analysis shows that the encryption method has not only strong robustness and security, but also good performance against various attacks.

Graphical Abstract

SPNA and CA results: (a) s1-DE-SPNA, (b) s2-DE-SPNA, (c) s3-DE-SPNA, (d) s4-DE-SPNA; (e) s1-DE-CA, (f) s2-DE-CA, (g) s3-DE-CA, (h) s4-DE-CA

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Funding

This research is supported by the National Natural Science Foundation of China (Nos: 61702356, 61672124 and 61503375), National Natural Science Foundation of ShanXi Province (Nos: 201801D121143), the Password Theory Project of the 13th Five-Year Plan National Cryptography Development Fund (No: MMJJ20170203).

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Authors and Affiliations

  1. College of Software, Taiyuan University of Technology, Jinzhong, 030600, China

    Xie Hong-wei & Zhang Yu-zhou

  2. College of Information and Computer, Taiyuan University of Technology, Jinzhong, 030600, China

    Li Zhen-yu & Zhang Hao

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  1. Xie Hong-wei
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  2. Zhang Yu-zhou
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  3. Li Zhen-yu
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  4. Zhang Hao
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Correspondence to Zhang Hao.

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Hong-wei, X., Yu-zhou, Z., Zhen-yu, L. et al. Color medical image cryptography technology based on segmentation and fractional-order hyperchaotic system. Med Biol Eng Comput 61, 109–127 (2023). https://doi.org/10.1007/s11517-022-02700-2

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  • DOI: https://doi.org/10.1007/s11517-022-02700-2

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