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Updated: Mar 23, 2026

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

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

Published on: November 30, 2022

Quality optimized medical image information hiding algorithm that employs edge detection and data coding.

Hayat Al-Dmour¹, Ahmed Al-Ani¹

¹School of Electrical, Mechanical and Mechatronic Systems, Faculty of Engineering and Information Technology, University of Technology Sydney, NSW 2007, Australia.

Computer Methods and Programs in Biomedicine

|March 23, 2016

Summary

This summary is machine-generated.

This study introduces a secure medical imaging system combining steganography and cryptography to embed patient data in medical images. The method ensures confidentiality and image quality, preserving diagnostic regions.

Keywords:

Cryptography Digital steganography EPR Edge detection Hamming code STC

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X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

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Published on: September 11, 2011

Area of Science:

Medical Imaging
Information Security
Data Privacy

Background:

Confidentiality of Electronic Patient Records (EPRs) is crucial.
Protecting sensitive medical data within images requires robust security measures.
Existing methods may compromise image quality or diagnostic regions.

Purpose of the Study:

To develop a secure medical imaging information system.
To combine steganography and cryptography for secure data embedding.
To embed patient confidential information into medical images without affecting diagnostic quality.

Main Methods:

Utilized a combined steganography and cryptography technique.
Concealed coded Electronic Patient Records (EPRs) into medical images, focusing on the Region of Non-Interest (RONI).
Employed edge detection for embedding in sharp regions and utilized Hamming code and Syndrome Trellis Code (STC) for efficient data embedding.

Main Results:

Successfully embedded large amounts of secret data without noticeable image distortion.
Demonstrated the effectiveness of the algorithm against steganalysis techniques.
Achieved imperceptible stego images with minimal embedding distortions.

Conclusions:

The developed system securely conceals EPR data within medical images.
The method preserves the Region of Interest (ROI), ensuring diagnostic integrity.
The system offers a secure and high-quality solution for medical data protection.