Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Ethical Standards I01:25

Ethical Standards I

The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
The Code of Ethics provisions outline the nurse's duty to the patient, the healthcare team, the profession, and society. The Code's fundamental principles include advocacy,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Neuro-symbolic reasoning engine for tax optimisation.

Frontiers in artificial intelligence·2026
Same author

DBpHash: a blockchain-based dual-band perceptual hashing framework for copyright protection of purely chromatic background images.

Scientific reports·2026
Same author

Saurashtra: An Indo-Aryan language dataset.

Data in brief·2026
See all related articles

Related Experiment Video

Updated: Jul 16, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

11.3K

A semantic bit-plane based three-layer encryption framework for secure medical images.

Harshini R1, Karthika Veeramani2

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, 600127, India.

Scientific Reports
|April 30, 2026
PubMed
Summary

A new three-layer medical image encryption algorithm enhances security for digital healthcare by decomposing images into sensitive layers. This method ensures lossless reconstruction for accurate diagnosis and robust protection against attacks.

Keywords:
Bit-plane analysisCryptographic diffusionMedical image securityMulti-layer encryptionMultimodal medical imaging

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.6K

Related Experiment Videos

Last Updated: Jul 16, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

11.3K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.6K

Area of Science:

  • Medical Imaging
  • Cybersecurity
  • Digital Health

Background:

  • Secure transmission and storage of sensitive patient medical images are paramount with the rise of digital healthcare, telemedicine, and cloud-based systems.
  • Existing security measures face challenges in balancing high security with the need for lossless reconstruction essential for accurate medical diagnosis.

Purpose of the Study:

  • To propose a novel three-layer medical image encryption algorithm designed for enhanced security and lossless reconstruction.
  • To differentiate protection levels based on the sensitivity of information within medical images by decomposing them into structural, detail, and fine information layers.

Main Methods:

  • Decomposition of medical images into three semantic layers (structural, detail, fine information) based on bit-planes.
  • Application of separate, key-dependent encryption processes for each layer using cryptographic keys derived from a single master key (SHA-256 based key derivation).
  • Integration of key-dependent substitution, block-level diffusion, keystream encryption, pixel chaining, and a global plaintext-dependent diffusion for robust security and computational efficiency.

Main Results:

  • High level of security demonstrated through an average entropy of 7.9998, near-zero correlation, NPCR of 99.61%, and UACI of 33.47%.
  • Perfect lossless reconstruction confirmed by an infinite Peak Signal to Noise Ratio (PSNR).
  • Effective resistance to statistical and differential attacks due to the multi-layered encryption and global diffusion process.

Conclusions:

  • The proposed three-layer medical image encryption algorithm offers a robust solution for securing sensitive patient data in digital healthcare environments.
  • The algorithm successfully balances high security with the critical requirement of lossless reconstruction, ensuring diagnostic accuracy.
  • This approach provides a scalable and efficient method for protecting medical images across various modalities.