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Hybrid blind re-watermarking approach for medical image security and annotation using LabVIEW.

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Title: Hybrid blind re-watermarking approach for medical image security and annotation using LabVIEW.
Authors: Kallel, Imen Fourati1 (AUTHOR) imen.fourati@enetcom.usf.tn, Kallel, Mohamed1 (AUTHOR) mohamed.kallel@enetcom.usf.tn
Source: Multimedia Tools & Applications. Nov2025, Vol. 84 Issue 39, p47855-47875. 21p.
Subjects: Digital watermarking, LabVIEW (Computer software), Machine learning, Data integrity, Signal-to-noise ratio, Image processing
Abstract: This paper is a suggestion about an hybrid blind re-watermarking approach for medical images security and annotation using Laboratory virtual instruments engineering workbench (LabVIEW). Three different watermarks are successively inserted into the cover medical image. A first signature, which presents hospital logo is used for authentication and verification in order to trace the origin of the medical image. The second one is a medical report integrated as a label to annotate the medical image. The work is ensured using an intelligent watermarking model by means of machine learning technique. The third is a hash sequence used for integrity verification, which ensures that the medical image has not been modified in any unauthorized way. The hybridization of the traditional and intelligent watermarking process is aimed not only to improve the method's performance but also to get reliable experimental results with an average PSNR of 44.65 dB and an important insertion capacity of 3 bits per pixel in addition to a high level of security. The high level of the functionalities' LabVIEW may lead to create a more speedy and accessible user interface offering doctors and practitioners a labeling and security tool for medical images. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: This paper is a suggestion about an hybrid blind re-watermarking approach for medical images security and annotation using Laboratory virtual instruments engineering workbench (LabVIEW). Three different watermarks are successively inserted into the cover medical image. A first signature, which presents hospital logo is used for authentication and verification in order to trace the origin of the medical image. The second one is a medical report integrated as a label to annotate the medical image. The work is ensured using an intelligent watermarking model by means of machine learning technique. The third is a hash sequence used for integrity verification, which ensures that the medical image has not been modified in any unauthorized way. The hybridization of the traditional and intelligent watermarking process is aimed not only to improve the method's performance but also to get reliable experimental results with an average PSNR of 44.65 dB and an important insertion capacity of 3 bits per pixel in addition to a high level of security. The high level of the functionalities' LabVIEW may lead to create a more speedy and accessible user interface offering doctors and practitioners a labeling and security tool for medical images. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11042-025-21033-4
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        Text: English
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        Type: general
      – SubjectFull: LabVIEW (Computer software)
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      – SubjectFull: Machine learning
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      – SubjectFull: Signal-to-noise ratio
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              Text: Nov2025
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