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Related Experiment Video

Updated: Jul 16, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Tamper detection and restoring system for medical images using wavelet-based reversible data embedding.

Kuo-Hwa Chiang1, Kuang-Che Chang-Chien, Ruey-Feng Chang

  • 1Institute of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan.

Journal of Digital Imaging
|March 3, 2007
PubMed
Summary

This study introduces two lossless systems to detect and restore forged medical images. These methods can identify and repair tampered image regions without needing the original, ensuring data integrity.

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Area of Science:

  • Digital Image Processing
  • Medical Imaging Security
  • Data Integrity

Background:

  • Digital images, including medical images, are increasingly vulnerable to imperceptible alterations by malicious actors.
  • The trustworthiness of digital medical images is compromised, necessitating robust protection against forgery.
  • Traditional verification methods often require the original image, which may not be available.

Purpose of the Study:

  • To propose novel systems for detecting and restoring forged medical images using lossless data-embedding techniques.
  • To develop methods capable of recovering either the entire image or a physician-specified region of interest.
  • To provide a means of verifying image authenticity and restoring original data without comparison to a pristine original.

Main Methods:

Related Experiment Videos

Last Updated: Jul 16, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

  • Implementation of two distinct detection and restoration systems based on lossless data-embedding.
  • System 1: Capable of recovering entire image blocks.
  • System 2: Focused on recovering specific regions of interest with enhanced visual quality.

Main Results:

  • Both systems successfully detect and locate forged image portions without requiring the original image.
  • The first system restores tampered images to a state extremely close to the original.
  • The second system achieves lossless recovery of physician-selected regions of interest.

Conclusions:

  • The proposed lossless data-embedding systems offer a viable solution for medical image forgery detection and restoration.
  • These methods enhance the security and trustworthiness of digital medical imaging.
  • The ability to restore specific regions of interest improves clinical utility and physician confidence.