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.
Highlights
Medical image processing has developed for many years, and there are more and more powerful tools to help dermatologists identify and classify skin lesions [1,2,3,4,5,6]
A novel approach for skin lesion detection and privacy protection is presented in this paper
A transition region-based level set evolution method is proposed to detect skin lesion image. is idea of the proposed method is to construct the energy functional that compels the level set function to have a different sign inside and outside the image target. en this functional is introduced into a variational level set expression with the other two functionals. en, once skin lesion detection has been performed, the detected skin lesion pixels are encrypted by utilizing DNA sequences and chaotic systems
Summary
Medical image processing has developed for many years, and there are more and more powerful tools to help dermatologists identify and classify skin lesions [1,2,3,4,5,6]. The technique based on neural networks cannot deal with noisy images as the details of the edge were broken Motivated by these problems, Barcelos et al [26] posed a detection model for skin lesion image starting from nonlinear diffusion equations. Inspired by the works of Wen et al [22, 30], but different from these works, this paper proposes a transition region(TR-) based energy functional which can compel the level set function to have opposite different inside and outside image target. (1) A novel skin lesion detection-based selective encryption method is proposed. (2) In the skin lesion detection scheme, we propose a transition region- (TR-) based energy functional which compels this function to have a different sign inside and outside the image target.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
