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Related Experiment Video

Updated: Oct 5, 2025

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

An efficient image encryption scheme for healthcare applications.

Parsa Sarosh¹, Shabir A Parah¹, G Mohiuddin Bhat²

¹Post Graduate Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India.

Multimedia Tools and Applications

|January 31, 2022

Summary

This study introduces an adaptive security framework for e-healthcare images using a 3D-chaotic system. The novel method enhances medical image confidentiality against sophisticated attacks, ensuring secure data transmission in digital health systems.

Keywords:

Big data Biomedical systems Healthcare Image Encryption Medical images Privacy Security

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Area of Science:

Medical Imaging Security
Applied Cryptography
Digital Health

Background:

Growing demand for secure image multimedia in healthcare.
Existing e-health security schemes lack adaptivity and resist specific attacks.
Need for robust protection of sensitive medical images during transmission.

Purpose of the Study:

To present an adaptive framework for preserving the security and confidentiality of e-healthcare images.
To develop a novel scheme resistant to chosen and known-plaintext attacks.
To enhance the security of medical image transmission in digital healthcare systems.

Main Methods:

Utilizes a 3D-chaotic system for keystream generation.
Employs 8-bit and 2-bit permutations and pixel diffusion via a Piecewise Linear Chaotic Map (PWLCM) generated key-image.
Incorporates image parameter calculation and criss-cross diffusion for enhanced security.

Main Results:

Achieved high Number of Pixels Change Rate (NPCR) of 99.5996% and Unified Average Changing Intensity (UACI) of 33.499% for 256x256 images.
Demonstrated average entropy of 7.9971 and Peak Signal to Noise Ratio (PSNR) of 7.4756.
Validated on 50 COVID-19 and viral pneumonia X-ray images, showing uniform histograms resistant to statistical attacks.

Conclusions:

The proposed adaptive framework effectively secures e-healthcare images against various attacks.
The scheme's ability to generate uniform histograms indicates strong resistance to statistical analysis.
The framework is suitable for application in AI-based healthcare systems requiring high image security.