QR Steganography for Information Hiding of Patient Record

DOI

10.2991/978-94-6463-094-7_30How to use a DOI?

Keywords

Steganography; EEG; ECG; SMQT; QR Code

Abstract

In recent research studies, biosignals are used to study the behaviour of a human body function which are useful for medical diagnosis. Biosignals such as electrocardiogram (ECG) signals are used to determine the irregularities in heartbeat meanwhile electroencephalogram (EEG) signal is used to record the brain activity of a patient. This paper aims to put together a mechanism to hide patient details with image of patient medical biosignals using steganography. Patient details are stored in the QR Code meanwhile biosignals that are in 1 dimensional are converted into two-dimensional image. In this process of hiding the patient details and its biosignal, fine details may be lost. Thus, image enhancement process is needed. In this paper, methods such as Local Laplacian filter, Successive Mean Quantization Transform (SMQT) algorithm, Non-Local Means filtering, Bilateral filtering with Gaussian Kernel and Anisotropic Diffusion are used to evaluate the medical image quality of the biosignal. Quantitative metrics are used to evaluate the quality of implementation. The proposed method has given out results of Peak Signal-to-Noise Ratio (PSNR), Mean-Squared Error (MSE), Normalized Cross-Correlation (NCC) that are comparatively well with other established methods. Findings from this paper indicates improvement in the overall image quality of biosignals after extraction from its cover image using the proposed methods.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Angkay Subramaniam
AU  - Wan-Noorshahida Mohd-Isa
AU  - Timothy Yap
PY  - 2022
DA  - 2022/12/27
TI  - QR Steganography for Information Hiding of Patient Record
BT  - Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
PB  - Atlantis Press
SP  - 392
EP  - 404
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-094-7_30
DO  - 10.2991/978-94-6463-094-7_30
ID  - Subramaniam2022
ER  -