A Novel Selective Encryption Method Based on Skin Lesion Detection

Dezhi An, Jun Lu, Shengcai Zhang and Yan Li

Mathematical Problems in Engineering, 2020, vol. 2020, 1-13

Abstract:

Due to the semitrusted cloud, privacy protection of medical images in medical imaging clouds has become a precondition. For the privacy of patients and the security of medical images in the cloud, this paper proposes a selective encryption based on DNA sequence and chaotic maps for skin lesion image. Initially, we design a transition region-based level set evolution functional which is merged into a variational level set expression with two extra energy functionals, to segment skin lesion image. Once skin lesion detection has been performed, the detected skin lesion pixels are encrypted by employing chaotic systems and DNA sequences. We apply 2D-LASM and 1D-LSS to produce the pseudorandom sequences and use the hash function of the plaintext image to calculate the secret keys of the encryption system. Results demonstrate that the proposed segmentation method is particularly suitable for the detection of skin lesion images with strong noise and complex background. Meanwhile, security analysis also reveals that this selective encryption has a large security key space and high sensitivity to the plaintext image and the secret key.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2020/7982192.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2020/7982192.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:7982192

DOI: 10.1155/2020/7982192

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().