# High-capacity DWT-SVD watermarking for MRI images
embedding MITR medical information

Aicha Benyoucef, Aya Goudjil, M'Hamed Hamadouche, Mohammed Chaker

Boutalbi, Mohammed Ammar, Mostafa El Habib Daho

► To cite this version:

Aicha Benyoucef, Aya Goudjil, M'Hamed Hamadouche, Mohammed Chaker Boutalbi, Mohammed Ammar, et al.. High-capacity DWT-SVD watermarking for MRI images embedding MITR medical information. Results in engineering, 2025, 27, pp.105795. 10.1016/j.rineng.2025.105795. hal-05237097

HAL Id: hal-05237097

https://hal.univ-brest.fr/hal-05237097v1

Submitted on 3 Sep 2025

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Research paper

# High-capacity DWT-SVD watermarking for MRI images embedding MITR medical information

Aicha Benyoucef<sup>a,*</sup>, Aya Goudjil<sup>a</sup>, M'Hamed Hamadouche<sup>a</sup>, Mohammed Chaker Boutalbi<sup>b</sup>, Mohammed Ammar<sup>c</sup>, Mostafa El Habib Daho<sup>d,e, ID, **</sup>

## ARTICLE INFO

Keywords:

Medical image test report

Digital watermarking

DWT-SVD

RONI

Imperceptibility

Capacity

## ABSTRACT

Securing Medical Imaging Test Reports (MITRs) during digital transmission is a growing concern in the era of telemedicine. Conventional watermarking methods often face a trade-off between imperceptibility, robustness, and payload capacity, especially in the context of sensitive medical data. To address this challenge, we propose an efficient and secure watermarking technique tailored for MRI brain images, using a combination of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD).

The core idea involves embedding four sub-watermarks—a QR-encoded MITR, patient photo, and hospital logos—into strategically selected Region of Non-Interest (RONI) blocks of the cover image, while preserving the diagnostic Region of Interest (ROI). This region-based design ensures both high payload capacity and minimal visual distortion, even under hardware constraints.

Experimental evaluations demonstrate that our method maintains high imperceptibility (PSNR > 67 dB, SSIM = 1.000), robustness (NC > 0.9430), and zero Bit Error Rate (BER = 0.1120) under common image processing attacks. Additionally, the use of QR codes for encoding the MITR improves the security and confidentiality of patient data. Compared to recent approaches, our method achieves better performance in both visual quality and robustness, confirming its effectiveness for secure medical image transmission in clinical and telehealth applications.

## 1. Introduction

The rapid advancement of e-health applications and telemedicine has significantly facilitated the exchange and accessibility of medical data, ushering in a transformative era in medical diagnosis. Given the sensitive nature of patient information, it is imperative to ensure its protection against unauthorized access, manipulation, and misuse. In addition, each medical imaging test inherently contains specific diagnostic information that varies with the acquisition modality. Securing such information during storage and transmission is essential to preserve diagnostic accuracy and maintain patient confidentiality.

Medical image security plays a critical role in upholding ethical standards, complying with legal regulations, and maintaining the trust-

worthiness of healthcare systems. Cyber threats—such as data breaches, unauthorized alterations, and adversarial attacks—pose substantial risks to the integrity and confidentiality of medical records. In response, legislative frameworks like the Health Insurance Portability and Accountability Act (HIPAA) and the Health Information Technology for Economic and Clinical Health (HITECH) Act were enacted. These acts mandate a comprehensive set of technical and administrative safeguards to ensure the confidentiality, integrity, and availability of electronic patient records (EPRs) [26].

Digital watermarking has emerged as a widely adopted strategy for ensuring the security of medical images. It involves embedding critical information into a host image in an imperceptible yet robust manner, enabling functionalities such as source authentication, tamper detection.

<footnote><sup>a</sup> LIMOSE Laboratory, M'Hamed Bougara Boumerdes University, Algeria</footnote>

<footnote><sup>b</sup> Kasdi Merbah Ouargla University, Algeria</footnote>

<footnote><sup>c</sup> LIST Laboratory, M'Hamed Bougara Boumerdes University, Algeria</footnote>

<footnote><sup>d</sup> LaTIM UMR 1101, Inserm, Brest, France</footnote>

<footnote><sup>e</sup> University of Western Brittany, Brest, France</footnote>

<footnote>* Corresponding author at: M'Hamed Bougara Boumerdes University, Boumerdes, Algeria</footnote>

<footnote>** Corresponding author at: University of Western Brittany, Brest, France.</footnote>

<footnote>E-mail addresses: a.benyoucef@univ-boumerdes.dz (A. Benyoucef), mostafa.elhabibdaho@univ-brest.fr (M. El Habib Daho).</footnote>
tion, and copyright protection. Watermarking methods can be broadly classified into two categories: (1) spatial domain techniques, which offer high imperceptibility and payload capacity but are generally less robust and highly dependent on image resolution, and (2) frequency domain techniques, which provide improved robustness and have become prevalent in medical imaging security. In the frequency domain, the Discrete Wavelet Transform (DWT) is commonly employed to decompose the Region of Non-Interest (RONI) in medical images, enabling targeted watermark embedding with minimal visual distortion [9]. The Discrete Cosine Transform (DCT) is utilized for efficient feature extraction and is often integrated with encryption techniques for enhanced security [28]. Additionally, Singular Value Decomposition (SVD) provides a hierarchical representation of image features, contributing to improved watermark resilience against common image processing attacks [10]. The Radon and Slantlet transforms are employed for secure and reversible watermarking, where the Radon transform ensures spatial obfuscation and the Slantlet transform enables frequency-domain embedding using pixel pair mapping, resulting in high imperceptibility and robustness [2]. These techniques collectively address the limitations of fragile watermarking and improve robustness [9,15].

To further enhance the security of embedded data, many studies propose the integration of watermarking with encryption schemes. This can involve encrypting the watermark prior to embedding or securing the watermarked image during transmission over unsecured channels. Various cryptographic approaches have been proposed in this context, including SVD combined with XOR encryption [38], chaotic cryptography [33], and Elliptic Curve Cryptography (ECC) [18], each aiming to strengthen confidentiality and access control in medical imaging workflows [23].

Recently, Quick Response (QR) codes have emerged as a promising tool in the domain of secure watermarking, particularly in mobile and telemedicine applications. In medical image watermarking, QR codes are typically used to encode sensitive information—such as hospital logos, patient identifiers, or diagnostic summaries—into a compact, machine-readable format prior to embedding into the image [25,30]. Beyond their utility for encryption and authentication, QR codes offer a practical solution to the capacity problem encountered in traditional watermarking schemes.

In [10], the authors embedded the Medical Image Test Report (MITR) in the medical image using a QR code to address payload capacity while achieving a high imperceptibility level (PSNR = 55.2132 dB). However, this approach only considered the MITR and did not include other essential sensitive information such as patient identity or hospital credentials. In addition, it lacks mechanisms for sender-receiver authentication, which is critical in medical data exchange.

## 1.1. Motivation and contributions of the proposed work

Although prior studies have made progress in watermarking medical images, many focus on embedding limited types of information, often only MITR, without addressing the broader scope of data security and authentication. These approaches typically struggle to maintain an effective balance between imperceptibility, payload capacity, and robustness. Furthermore, most fail to include provisions to securely identify both the patient and the medical institution. These limitations underscore the need for a more comprehensive solution, which forms the basis for the enhancements proposed in this work.

The main contributions of this work are summarized as follows:

* **Enhanced payload and security:** The MITR content is first encoded into a compact QR code, which significantly reduces its size regardless of the original report dimensions. This improves the watermark payload capacity while ensuring the security and confidentiality of patient data.

* **High imperceptibility:** Despite the increased payload, the proposed method achieves excellent visual quality, with Peak Signal-

to-Noise Ratio (PSNR) values exceeding 67 dB, indicating minimal perceptual distortion in the watermarked medical image.

* **Robustness and authentication:** The proposed method demonstrates resilience against various signal processing attacks. Furthermore, the inclusion of the hospital logo as a watermark ensures image authenticity and institutional verification.

## 1.2. Organization of the paper

The remainder of this paper is structured as follows: Section 2 reviews the related literature on medical image watermarking. Section 3 introduces the preliminary concepts and background knowledge necessary to understand the proposed method. Section 4 presents the details of the proposed frequency domain watermarking approach. Section 5 reports the experimental results and performance analysis. Section 6 provides a comprehensive discussion of the method's strengths and limitations. Finally, a conclusion that concludes the paper and outlines future directions.

## 2. Related work

Digital watermarking has evolved significantly as a key technique for securing multimedia content transmitted over the Internet. It involves concealing information within a host signal, such as an image, audio, or video file, in a manner that ensures its integrity, authenticity, or confidentiality [43]. The visibility of the embedded watermark depends on whether it can be perceived by the human visual system (HVS). If the watermark is perceptible, the method is categorized as visible watermarking; otherwise, it is referred to as invisible watermarking [39,21,34].

Digital watermarking techniques can be further classified as either reversible or non-reversible. Reversible (or lossless) watermarking allows for the complete restoration of the original host signal after watermark extraction. This is particularly critical in domains such as medical imaging and legal documentation, where data integrity is paramount [11,20,50,6,35,31]. By contrast, non-reversible (or irreversible) watermarking techniques introduce permanent changes to the host signal. These methods prioritize robustness over reversibility and are commonly used in copyright protection and anti-piracy applications [42].

In the spatial domain, techniques such as Least Significant Bit (LSB) substitution and Local Binary Pattern (LBP) encoding have been extensively used due to their simplicity, high payload capacity, and low computational complexity [14,16,29]. However, these methods tend to be fragile and vulnerable to common image processing operations.

To address the limitations of spatial domain techniques, researchers have proposed watermarking schemes in the frequency domain, utilizing transformations such as the DWT, DCT, SVD, and Discrete Fourier Transform (DFT) [7,17,27,44]. These approaches are typically more robust against compression, noise, and filtering operations. In recent studies, hybrid methods combining two or more frequency domain techniques (e.g., DWT-SVD or DWT-DCT) have also been proposed to enhance robustness, integrity, and authentication [13,4].

To reinforce the security of watermarking systems, cryptographic mechanisms are frequently integrated into the watermarking pipeline. This includes encrypting the watermark prior to embedding or encrypting the watermarked image prior to transmission over unsecured networks [32]. Among various encryption approaches, QR codes have recently gained attention for encoding hospital logos, patient metadata, or diagnostic content into a compact two-dimensional barcode format [45]. QR codes are efficient for storing encrypted information and can be easily embedded into the watermarking process.

Another important design consideration in medical image watermarking is the embedding location. Watermarks can be inserted either across the entire image (global embedding) or selectively within a specific region. Region-based watermarking strategies typically segment the image into the Region of Interest (ROI) and the RONI, in order to preserve critical diagnostic information while embedding auxiliary data
<figure><img src="image_2.png" /><figcaption>Fig. 1. Example of a brain MRI image.</figcaption></figure>

into less sensitive areas. Segmentation of ROI and RONI can be achieved either manually or via automated methods, such as the Active Contour Model (ACM) [13].

## 3. Background and preliminaries

This section presents the components of the Magnetic Resonance Imaging (MRI) diagnostic workflow and describes the extracted data types, including medical images, patient information, and MRI test reports. Furthermore, the techniques utilized in the proposed medical image watermarking method are introduced.

### 3.1. Brain MRI and associated medical information

MRI is a non-invasive imaging technique widely employed for the diagnosis and monitoring of various neurological and systemic conditions. MRI provides multimodal insights into anatomical structure, metabolism, and function, making it invaluable in clinical and research contexts [8]. A brain MRI, also referred to as a head MRI, generates high-resolution images of intracranial structures using radiofrequency pulses, powerful magnetic fields, and computer-assisted reconstruction algorithms.

Clinicians may prescribe brain MRIs to detect and monitor conditions such as tumors, hemorrhages, inflammation, vascular anomalies, or neurodegenerative disorders such as Alzheimer's disease [36]. The use of contrast agents enhances the visibility of specific structures, including blood vessels and lesions. MRI also captures adjacent anatomical features such as the inner ear, optic nerves, skull, and facial musculature.

#### 3.1.1. MRI images

Fig. 1 illustrates a representative brain MRI image. MRI images contain detailed spatial representations of brain tissue and pathology, and are typically divided into:

* ROI: the medically significant area (e.g., tumors, lesions), where alterations due to watermark embedding could jeopardize diagnostic accuracy.

* RONI: typically the dark background, which is clinically irrelevant and therefore suitable for watermark embedding.

#### 3.1.2. MRI examination report

The MITR is a formal diagnostic report generated by a radiologist. It includes interpretations of the MRI findings, relevant clinical context, and personal patient information. Although this document is critical for clinical decision-making, it often lacks integrated protection mechanisms. Consequently, it remains vulnerable to unauthorized access or tampering during transmission across hospital networks (see Fig. 2).

### 3.2. Quick response code

QR code is a two-dimensional matrix barcode composed of black and white square modules. It serves as a machine-readable optical label that can encode and store a significant amount of information efficiently. QR codes are valued for their fast readability, error correction capability, multi-language support, and high data density [46,41].

# Patient medical imaging test report

Patient : xxxxxx xxxxxx

Id patient : 11111111

Téléphone : 00000000

## Indications :

Clinique = TR : nodule palpable ? : T1 ou T2 ; doute sur extension : T3 ?

Taux de PSA = ng/ml

Résultats des biopsies (date) = nombre de sextans envahis, pourcentage d'atteinte des carottes, score de Gleason.

## Technique :

Séquences axiale T2, frontale T2, axiale T1, axiale T1 dynamiques avec injection intraveineuse de gadolinium.

## Conditions d'examen :

Bonnes, moyennes, maîtrises.

Artéfacts : out/non, type

Séquelles biopsiques en pondération T1 : out/non.

## Résultats :

Volume prostatique (hauteur x largeur x épaisseur en cm / 2) = cm soit cm3.

## Analyse du signal :

⇒ De la zone périphérique :

* Plage suspecte : out/non

* Nombre, signal T2 et après gado, localisations : sextants

* pour chaque lésion : mensurations, volume, forme, contact capsulaire, zone transitionnelle

* Extension capsulaire : non/possible/probable

* contact

* déformation régulière

* déformation irrégulière

* comblement d'un angle prostato-rectal

* comblement d'une bandelette neuro-vasculaire

* Extension aux vésicules séminales : non/possible/probable

* localisation par rapport aux canaux éjaculateurs

* anomalie de signal vésiculaires : uni-bilatéral ; focal-diffus

⇒ De la zone transitionnelle :

* Hyperplasie bénigne : out/non

* volume

* lobe médian

* Plage suspecte : out/non

* forme

* signal T2, PDC sur la séquence dynamique

* Attente un ou deux lobes

* Attente du stroma fibro-musculaire antérieur

⇒ Attente ganglionnaire : non/possible/probable : Ganglion de grand axe > 8 mm, localisation

⇒ Vessie, rectum, uretère : non/possible/probable.

## Conclusion :

Compte tenu du contexte de risque d'extension (clinique, PSA, Gleason) :

Faible : PSA ≤ 10 ng/ml ; Gleason ≤ 6

Intermédiaire : PSA 10 - 20 ng/ml ; Gleason ≤ 7

Elevé : PSA > 20 ng/ml ; Gleason > 7

Visibilité d'une lésion évocatrice d'une néoplasie postérieure/antérieure/mixte occupant sextans/lobes avec envahissement absent/possible/probable de la capsule, absent/possible/probable de une/deux vésicules séminales, avec envahissement absent/possible/probable de une/deux bandelettes neurovasculaires.

Adenémie associée : out/non

Autres : out/non

Staging TNM IRM :

<caption>Fig. 2. Sample Medical Image Test Report (in French).</caption>

### 3.2.1. Applications in secure communication

QR codes are commonly used in mobile applications to facilitate tasks such as URL access, contact sharing, Wi-Fi connection, and software downloads. In secure communication, they support identity authentication, tamper detection, encrypted data transfer, and have been employed in both steganography and watermarking applications [47]. Their compact form and robustness make them an attractive candidate for embedding sensitive metadata in medical imaging workflows.

### 3.3. Discrete wavelet transform

The DWT is a widely used mathematical transformation in image processing, capable of decomposing an image into hierarchical sub-bands. A single-level DWT decomposes an image into four sub-bands:

* LL: Approximation sub-band containing low-frequency components and most of the image energy.

* LH, HL, HH: Detail sub-bands capturing vertical, horizontal, and diagonal high-frequency components, respectively [12,24].

Subsequent levels of DWT decomposition can be applied recursively to the LL sub-band, producing deeper multiscale representations (see Fig. 3).

DWT is extensively used for image compression, denoising, and watermarking. In watermarking applications, embedding in the LL sub-
<figure><img src="image_3.png" /><figcaption>Fig. 3. Two-level decomposition using the DWT.</figcaption></figure>

band increases robustness against common attacks (e.g., compression, noise), while embedding in high-frequency sub-bands ensures better im-perceptibility [3]. Its multiresolution and energy compaction properties also make DWT an essential component in AI-based medical image analysis pipelines [5,37].

### 3.4. Singular value decomposition

The SVD is a linear algebra technique that factorizes a real or complex matrix into three component matrices:

$$M = U S V^T \quad (1)$$

Where M is the original matrix, U and V are orthogonal matrices containing the left and right singular vectors, and S is a diagonal matrix of non-negative singular values:

$$S = \begin{vmatrix} S1 & & & & \\ & S2 & & & \\ & & \ddots & & \\ & & & S_n & \\ & & & & S_n \end{vmatrix} \quad (2)$$

SVD is effective for dimensionality reduction, data compression, and feature extraction, as it preserves the essential structure of data using a limited number of singular values [22,40]. In medical image watermarking, SVD is used to embed watermark data into the singular value matrix S, after which inverse SVD is applied to reconstruct the watermarked image [49]. Its robustness to image processing attacks, minimal perceptual distortion, and applicability to non-square matrices make it a valuable tool in watermarking applications [48,1,19].

## 4. Proposed method

In recent years, the importance of maintaining privacy and ensuring the security of EPRs and medical images has significantly increased. Unauthorized access to these records can have serious financial and medical consequences for patients. Given the diversity of formats included in EPRs, such as images, documents, and other multimedia data, securing the entire patient file poses a significant challenge.

To address this issue, this paper presents a watermarking technique specifically designed for medical image protection and EPR confidentiality. The proposed method offers a high embedding capacity, preserves the visual quality of the watermarked image, and enables authentication of the imaging institution. It is based on embedding the encoded MRI test report—converted into a QR code—along with the patient's photo and logos of both the sender and receiver hospitals, into the RONI of a brain MRI image using the DWT and SVD. The complete process is illustrated in Fig. 4, which outlines the main phases: data collection and watermark generation, embedding, and watermark extraction.

### 4.1. Data collection and treatment

The data collection phase begins when the patient undergoes a brain MRI scan. This diagnostic imaging procedure captures detailed anatomical structures of the brain, allowing clinicians to detect potential abnormalities such as tumors, hemorrhages, or degenerative diseases. These images, together with the examination report prepared by a radiologist, form the basis of the data to be protected. Fig. 4 (data collection block) summarizes this acquisition process.

### 4.2. Watermark image generation

Watermark generation is a critical step, especially when the embedded information must remain both secure and compact. This process consists of two main stages: (1) transforming the MITR into a QR code and assembling it with other sub-watermark images, and (2) segmenting the medical image to isolate the RONI for watermark embedding. The procedure is illustrated in Algorithm 1 and the corresponding block in Fig. 4.

#### 4.2.1. The QR code of the MITR

The use of QR codes serves two purposes: (1) it encodes the content of the MITR and converts it into a fixed-size image, and (2) it reduces payload capacity while maintaining imperceptibility. Unlike LSB-based methods, which may degrade image quality even for small payloads, QR-based embedding provides high information density and robustness. Fig. 5 illustrates the generated QR code corresponding to the MITR.

The complete watermark image is constructed by concatenating four grayscale 64 × 64 images (QR code, patient photo, sender and receiver hospital logos) into a single 128 × 128 watermark, as shown in Fig. 5.

#### 4.2.2. RONI segmentation and medical image preprocessing

Following a sharpening filter applied to the MRI image, segmentation is performed to distinguish the ROI from the RONI. The ROI includes diagnostically relevant regions such as lesions, while the RONI comprises non-informative background pixels suitable for watermarking.

A region-based active contour model primarily based on the Chan-Vese method, which differs from traditional snake models by not relying on parameters such as alpha, beta, or gamma. Instead, it segments images by evolving a contour to minimize a region-based energy functional that separates the image into foreground and background regions. The key inputs for this function include the initial mask, which defines the starting region for contour evolution, the number of iterations, controlling how long the contour evolves, and the method parameter, which can be set to 'Chan-Vese' (default) for region-based segmentation or 'edge' for edge-based segmentation. This approach is especially effective in images where object boundaries are not clearly defined by edges alone.

The model was iterated 200 times. A sharpening filter was applied to the image to reduce noise and enhance edge detection before contour evolution.

The RONI is then defined as the complement of this segmented region. Fig. 6 shows the ROI segmentation and the resulting RONI.

### 4.3. Embedding process

The embedding procedure integrates the watermark image into the RONI using DWT and SVD, as detailed in Algorithm 2 and shown in Fig. 4 (embedding block). The RONI of size 512 × 512 is divided into four non-overlapping blocks of 256 × 256, each corresponding to one sub-watermark image.

**DWT and SVD decomposition.** Each RONI block undergoes 2-level DWT, resulting in LL2, LH2, HL2, and HH2 sub-bands. Meanwhile, each sub-watermark undergoes 1-level DWT, producing corresponding sub-bands
<figure><img src="image_4.png" /><figcaption>Fig. 4. The proposed watermarking process is divided into four main stages: data collection, watermark generation, embedding, and extraction. The first block ensures proper acquisition and preprocessing of MRI and MITR data. The watermark is generated from sensitive medical information and securely embedded using DWT-SVD techniques. Finally, the extraction block retrieves the watermark.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 5. The 4 sub-watermark images: sender hospital logo, QR code, receiver hospital logo, patient photo.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 6. MRI image, segmented ROI, and extracted RONI (from left to right).</figcaption></figure>

### Algorithm 1 Preparation Phase.

**Require:** MRI image, patient photo, sender and receiver hospital logos, and the MITR document

**Ensure:** Segmented RONI, QR code of MITR, and final watermark image (WI)

1. **RONI Segmentation:**

2. Read the MRI image, resize to 512 × 512, and apply sharpening

3. Apply active contour segmentation to extract the ROI

4. Subtract ROI from the image to isolate the RONI

5. **Watermark Generation:**

6. Generate a QR code image from the MITR document

7. Convert the patient image and logos to grayscale and resize to 64 × 64

8. Concatenate the four sub-images (QR code, patient image, two logos) into a 128 × 128 watermark image (WI)

(LL, LH, HL, HH). The LL and LL2 sub-bands are then decomposed using SVD:

$$LL_2 = U_{LL2} \cdot S_{LL2} \cdot V_{LL2}^T \quad (3)$$

$$LL = U_{WI} \cdot S_{WI} \cdot V_{WI}^T \quad (4)$$

**Embedding operation.** For each block, the watermark is embedded by modifying the singular values:

$$S_0 = S_{LL2} + \alpha \cdot S_{WI} \quad (5)$$

where $\alpha$ is the scaling factor controlling watermark strength. Values $\alpha = \{0.001, 0.01, 0.05\}$ are experimentally evaluated.

The modified singular matrix $S_0$ is used to reconstruct the watermarked LL2, followed by inverse DWT to obtain the watermarked RONI. The final watermarked image (MIW) is obtained by concatenating the unchanged ROI with the watermarked RONI.

### 4.4. Extracting process

The extraction process reverses the embedding steps, as illustrated in Fig. 4 and summarized in Algorithm 3. The RONI' is segmented using

### Algorithm 2 Embedding Process.

**Require:** RONI, QR code, patient photo, sender and receiver hospital logos

**Ensure:** Watermarked MRI image (MIW)

1. Split RONI into 4 blocks (A, B, C, D) of size 256 × 256

2. **for each block do**

3. Apply 2-level DWT to get sub-bands: LL2, LH2, HL2, HH2

4. Apply SVD on LL2: $LL2 = U_{LL2} \cdot S_{LL2} \cdot V_{LL2}^T$

5. **end for**

6. **for each sub-watermark (QR, photo, logos) do**

7. Apply 1-level DWT

8. Apply SVD on LL band: $LL = U_{WI} \cdot S_{WI} \cdot V_{WI}^T$

9. **end for**

10. Choose embedding strength $\alpha$ (e.g., $\alpha = 0.01$)

11. **for each block do**

12. Embed: $S_0 = S_{LL2} + \alpha \cdot S_{WI}$

13. Apply inverse SVD and inverse 2-level DWT

14. **end for**

15. Reconstruct RONI' from watermarked blocks

16. Merge ROI and RONI' to obtain the final watermarked MRI image (MIW)

ACM and divided into four blocks. Each block undergoes 2-level DWT followed by SVD to retrieve the modified singular values $S_0$.

**Extraction of singular values.** The watermark's singular values are recovered as:

$$S_{WI} = \frac{S_0 - S_{LL2}}{\alpha} \quad (6)$$

Using the previously stored $U_{WI}$ and $V_{WI}$ matrices, the watermark is reconstructed by inverse SVD and DWT. The four recovered watermark components include the QR code, which can be decoded to recover the MITR document.

### Algorithm 3 Extraction Process.

**Require:** Watermarked MRI image (MIW)

**Ensure:** Reconstructed watermark images: QR code, patient photo, sender and receiver hospital logos

1. Segment MIW into ROI and RONI' using active contour model (ACM)

2. Split RONI' into 4 blocks

3. **for each block do**

4. Apply 2-level DWT to extract LL2

5. Apply SVD on LL2: $LL2 = U_{LL2} \cdot S_{LL2} \cdot V_{LL2}^T$

6. Estimate embedded singular values: $S_{WI} = \frac{S_0 - S_{LL2}}{\alpha}$

7. Reconstruct watermark LL band: $LL_{WI} = U_{WI} \cdot S_{WI} \cdot V_{WI}^T$

8. Apply inverse DWT using $LL_{WI}$, LH, HL, HH to recover watermark block

9. **end for**

10. Combine the 4 watermark blocks into the final watermark image (WI)

11. Decode the QR code to retrieve the MITR document

## 5. Results and analysis

This section presents a detailed evaluation of the proposed medical image watermarking method. The method is assessed based on its imperceptibility, robustness, and computational efficiency. Experiments were conducted using a system with a Core i3 processor (1.70 GHz) and 4 GB of memory. The dataset used, the test results, and comparisons with existing techniques are described below.

### 5.1. Dataset details

The proposed method was evaluated on the SAMAH-HAFSA dataset, available on Kaggle, which includes brain MRI images categorized by tumor type (glioma, meningioma, pituitary) and non-tumor. A total of 50 images were resized to 512×512 pixels for uniform processing, as shown in Fig. 7.
## MRI Brain Images

<figure><img src="image_7.png" /><figcaption>Fig. 7. Medical images used for testing (Brain MRI Images).</figcaption></figure>

### 5.2. Imperceptibility test

In medical imaging, visual fidelity is paramount since even minimal alterations can influence clinical diagnosis. Imperceptibility ensures that the original and watermarked images are indistinguishable to the human visual system. To quantify imperceptibility, we use PSNR and Structural Similarity Index (SSIM), defined in Equations (7)–(9):

$$PSNR(I, I_w) = 10 \cdot \log_{10} \left( \frac{MAX_I^2}{MSE} \right) \qquad (7)$$

$$MSE = \frac{1}{M \cdot N} \sum_{i=1}^{M} \sum_{j=1}^{N} [I(i, j) - I_{w}(i, j)]^2 \qquad (8)$$

## Original MRI Brain Images

<figure><img src="image_8.png" /><figcaption>Fig. 8. A visual comparison between the original and watermarked MRI images under various scaling factors to assess visual quality and robustness.</figcaption></figure>

$$SSIM(X, Y) = \frac{(2\mu_x\mu_y + c_1)(2\sigma_{xy} + c_2)}{(\mu_x^2 + \mu_y^2 + c_1)(\sigma_x^2 + \sigma_y^2 + c_2)} \qquad (9)$$

where I and $I_w$ denote original and watermarked images, $\mu_x$ and $\mu_y$ are mean intensities, $\sigma_x$ and $\sigma_y$ are standard deviations, $\sigma_{xy}$ is the covariance, and $c_1$, $c_2$ are constants to stabilize division.

Table 1 presents PSNR, SSIM, and MSE values across different scaling factors ($\alpha$ = 0.001, 0.01, 0.05). The method maintains excellent imperceptibility, achieving average PSNR values above 67 dB for $\alpha$ = 0.001, and above 33 dB even for $\alpha$ = 0.05. SSIM values remain consistently above 0.98.

### 5.3. Robustness test

Robustness refers to the watermark's ability to withstand various image processing and noise attacks during transmission. We tested against salt-and-pepper, Gaussian, and speckle noise, as well as sharpening, Wiener, and median filters. The normalized correlation (NC) defined in Equation (10) is used to quantify robustness:

$$NC(W, \hat{W}) = \frac{\sum_{i=1}^{m} \sum_{j=1}^{n} W(i, j) \cdot \hat{W}(i, j)}{\sum_{i=1}^{m} \sum_{j=1}^{n} W(i, j)^2} \qquad (10)$$

Table 2 shows NC values for watermark extraction under no attacks. Tables 3 and 4 present the robustness results under noise and image processing attacks, respectively. All results confirm the watermark's resilience, with NC values ranging between 0.91 and 0.94 (Fig. 8).

To evaluate also the resilience of a watermarking algorithm against various types of attacks, the Bit Error Rate (BER) serves as a critical metric. It quantifies the proportion of incorrect bits by comparing the extracted watermark to the original after the watermark has been subjected to potential distortions. A robust watermarking technique should maintain accurate data retrieval despite such attacks. BER is defined as:

$$BER = \frac{\textit{Number of Errors}}{\textit{Total Number of Transmitted bits}} \qquad (11)$$

BER values range between 0 and 1, where a lower BER indicates stronger robustness and minimal degradation in watermark quality. In contrast, a higher BER (approaching 1) reflects a higher number of errors in the
<caption>**Table 1**</caption>

A comparison of PSNR, MSE, and SSIM metrics between the original and watermarked MRI images for different α values, demonstrating how embedding strength affects image quality.

<table><thead><tr><th rowspan="2">Image</th><th colspan="3">α=0.001</th><th colspan="3">α=0.01</th><th colspan="3">α=0.05</th></tr><tr><th>PSNR</th><th>MSE</th><th>SSIM</th><th>PSNR</th><th>MSE</th><th>SSIM</th><th>PSNR</th><th>MSE</th><th>SSIM</th></tr></thead><tbody><tr><td>001</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9997</td><td>33.9231</td><td>0.0004</td><td>0.9914</td></tr><tr><td>002</td><td>67.9025</td><td>0.0000</td><td>0.9999</td><td>47.9025</td><td>0.0000</td><td>0.9925</td><td>33.9231</td><td>0.0004</td><td>0.8867</td></tr><tr><td>003</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9999</td><td>33.9231</td><td>0.0004</td><td>0.9970</td></tr><tr><td>004</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9996</td><td>33.9231</td><td>0.0004</td><td>0.9892</td></tr><tr><td>005</td><td>67.9025</td><td>0.0000</td><td>0.9999</td><td>47.9025</td><td>0.0000</td><td>0.9891</td><td>33.9231</td><td>0.0004</td><td>0.9573</td></tr><tr><td>006</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9988</td><td>33.9231</td><td>0.0004</td><td>0.9752</td></tr><tr><td>007</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9980</td><td>33.9231</td><td>0.0004</td><td>0.9671</td></tr><tr><td>008</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9998</td><td>33.9231</td><td>0.0004</td><td>0.9937</td></tr><tr><td>009</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9991</td><td>33.9231</td><td>0.0004</td><td>0.9680</td></tr><tr><td>010</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9993</td><td>33.9231</td><td>0.0004</td><td>0.9820</td></tr><tr><td>011</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9997</td><td>33.9231</td><td>0.0004</td><td>0.9911</td></tr><tr><td>012</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9998</td><td>33.9231</td><td>0.0004</td><td>0.9939</td></tr><tr><td>013</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9998</td><td>33.9231</td><td>0.0004</td><td>0.9961</td></tr><tr><td>014</td><td>67.9025</td><td>0.0000</td><td>0.9999</td><td>47.9025</td><td>0.0000</td><td>0.9913</td><td>33.9231</td><td>0.0004</td><td>0.9270</td></tr><tr><td>015</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9998</td><td>33.9231</td><td>0.0004</td><td>0.9935</td></tr><tr><td>016</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9992</td><td>33.9231</td><td>0.0004</td><td>0.9770</td></tr><tr><td>017</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9986</td><td>33.9231</td><td>0.0004</td><td>0.9726</td></tr><tr><td>018</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9980</td><td>33.9231</td><td>0.0004</td><td>0.9500</td></tr><tr><td>019</td><td>67.9025</td><td>0.0000</td><td>0.9999</td><td>47.9025</td><td>0.0000</td><td>0.9886</td><td>33.9231</td><td>0.0004</td><td>0.9002</td></tr><tr><td>020</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9983</td><td>33.9231</td><td>0.0004</td><td>0.9680</td></tr><tr><td>021</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9994</td><td>33.9231</td><td>0.0004</td><td>0.9793</td></tr><tr><td>022</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9977</td><td>33.9231</td><td>0.0004</td><td>0.9290</td></tr><tr><td>023</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9997</td><td>33.9231</td><td>0.0004</td><td>0.9903</td></tr><tr><td>024</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9986</td><td>33.9231</td><td>0.0004</td><td>0.9666</td></tr><tr><td>025</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9984</td><td>33.9231</td><td>0.0004</td><td>0.9680</td></tr><tr><td>026</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9990</td><td>33.9231</td><td>0.0004</td><td>0.9780</td></tr><tr><td>027</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9966</td><td>33.9231</td><td>0.0004</td><td>0.9448</td></tr><tr><td>028</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9991</td><td>33.9231</td><td>0.0004</td><td>0.9801</td></tr><tr><td>029</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9979</td><td>33.9231</td><td>0.0004</td><td>0.9466</td></tr><tr><td>030</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9958</td><td>33.9231</td><td>0.0004</td><td>0.9481</td></tr><tr><td>031</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9996</td><td>33.9231</td><td>0.0004</td><td>0.9897</td></tr><tr><td>032</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9991</td><td>33.9231</td><td>0.0004</td><td>0.9759</td></tr><tr><td>033</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9992</td><td>33.9231</td><td>0.0004</td><td>0.9787</td></tr><tr><td>034</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9995</td><td>33.9231</td><td>0.0004</td><td>0.9882</td></tr><tr><td>035</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9985</td><td>33.9231</td><td>0.0004</td><td>0.9674</td></tr><tr><td>036</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9993</td><td>33.9231</td><td>0.0004</td><td>0.9834</td></tr><tr><td>037</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9994</td><td>33.9231</td><td>0.0004</td><td>0.9820</td></tr><tr><td>038</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9995</td><td>33.9231</td><td>0.0004</td><td>0.9874</td></tr><tr><td>039</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9992</td><td>33.9231</td><td>0.0004</td><td>0.9742</td></tr><tr><td>040</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9992</td><td>33.9231</td><td>0.0004</td><td>0.9836</td></tr><tr><td>041</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9996</td><td>33.9231</td><td>0.0004</td><td>0.9898</td></tr><tr><td>042</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9994</td><td>33.9231</td><td>0.0004</td><td>0.9849</td></tr><tr><td>043</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9941</td><td>33.9231</td><td>0.0004</td><td>0.9296</td></tr><tr><td>044</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9994</td><td>33.9231</td><td>0.0004</td><td>0.9851</td></tr><tr><td>045</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9993</td><td>33.9231</td><td>0.0004</td><td>0.9815</td></tr><tr><td>046</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9944</td><td>33.9231</td><td>0.0004</td><td>0.9457</td></tr><tr><td>047</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9991</td><td>33.9231</td><td>0.0004</td><td>0.9826</td></tr><tr><td>048</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9983</td><td>33.9231</td><td>0.0004</td><td>0.9611</td></tr><tr><td>049</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9991</td><td>33.9231</td><td>0.0004</td><td>0.9763</td></tr><tr><td>050</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.9955</td><td>33.9231</td><td>0.0004</td><td>0.9385</td></tr><tr><td>Average</td><td>67.9025</td><td>0.0000</td><td>1.0000</td><td>47.9025</td><td>0.0000</td><td>0.994312</td><td>33.9231</td><td>0.0004</td><td>0.98461</td></tr></tbody></table>

extracted data, signifying a lower resistance to tampering or degradation.

## 5.4. Computational complexity

Efficiency is vital for real-time deployment. Table 5 shows the time required for watermark embedding and extraction across different α values. Embedding is fast (~0.3 s), and extraction remains under 2.3 seconds, demonstrating suitability for real-time medical applications.

## 5.5. Comparative analysis

The proposed method is compared to several state-of-the-art watermarking approaches in Table 6, considering segmentation, embedding region, used techniques, reversibility, and achieved strengths. In Table 7, performance metrics (PSNR, SSIM, NC, and Bit Error Rate (BER)) confirm the superiority of our method in imperceptibility and robustness, while maintaining reversibility and authenticity.

## 6. Discussion

Based on the comprehensive evaluation conducted, the key strengths of the proposed watermarking approach can be summarized as follows.

**First**, the method achieves a PSNR greater than 67 dB and an SSIM value of 1 for most of the MRI brain images when the scaling factor α = 0.001, as shown in Table 1. These metrics confirm the method's excellent imperceptibility, indicating that the watermarked images are virtually indistinguishable from the originals.

**Second**, the robustness of the method has been validated under a variety of image processing and noise-based attacks. The effectiveness
<caption>**Table 2**</caption>

The NC values between the original and extracted watermark without attacks, indicating the accuracy and reliability of the watermark extraction process.

<table><caption>**Table 3**</caption><thead><tr><th>Image</th><th>α=0.001</th><th>α=0.01</th><th>α=0.05</th></tr></thead><tbody><tr><td>001</td><td>0.9162</td><td>0.9174</td><td>0.9224</td></tr><tr><td>002</td><td>0.9430</td><td>0.9433</td><td>0.9430</td></tr><tr><td>003</td><td>0.8215</td><td>0.8235</td><td>0.8302</td></tr><tr><td>004</td><td>0.9017</td><td>0.9007</td><td>0.8947</td></tr><tr><td>005</td><td>0.8167</td><td>0.8718</td><td>0.8796</td></tr><tr><td>006</td><td>0.8593</td><td>0.7910</td><td>0.7904</td></tr><tr><td>007</td><td>0.8500</td><td>0.8522</td><td>0.8623</td></tr><tr><td>008</td><td>0.8609</td><td>0.8627</td><td>0.8711</td></tr><tr><td>009</td><td>0.8990</td><td>0.9002</td><td>0.9102</td></tr><tr><td>010</td><td>0.9008</td><td>0.9012</td><td>0.9014</td></tr><tr><td>011</td><td>0.8150</td><td>0.8156</td><td>0.8162</td></tr><tr><td>012</td><td>0.8435</td><td>0.8459</td><td>0.8606</td></tr><tr><td>013</td><td>0.8652</td><td>0.8658</td><td>0.8660</td></tr><tr><td>014</td><td>0.9226</td><td>0.9226</td><td>0.9211</td></tr><tr><td>015</td><td>0.8526</td><td>0.8507</td><td>0.8410</td></tr><tr><td>016</td><td>0.8101</td><td>0.8084</td><td>0.8700</td></tr><tr><td>017</td><td>0.9048</td><td>0.9060</td><td>0.9109</td></tr><tr><td>018</td><td>0.8950</td><td>0.8962</td><td>0.8944</td></tr><tr><td>019</td><td>0.8942</td><td>0.8942</td><td>0.8957</td></tr><tr><td>020</td><td>0.9114</td><td>0.9105</td><td>0.9081</td></tr><tr><td>021</td><td>0.9263</td><td>0.9259</td><td>0.9210</td></tr><tr><td>022</td><td>0.9240</td><td>0.9245</td><td>0.9260</td></tr><tr><td>023</td><td>0.8578</td><td>0.8506</td><td>0.8258</td></tr><tr><td>024</td><td>0.8871</td><td>0.8878</td><td>0.8917</td></tr><tr><td>025</td><td>0.8622</td><td>0.8635</td><td>0.8688</td></tr><tr><td>026</td><td>0.9076</td><td>0.9078</td><td>0.9085</td></tr><tr><td>027</td><td>0.9100</td><td>0.9115</td><td>0.9169</td></tr><tr><td>028</td><td>0.9169</td><td>0.9182</td><td>0.9234</td></tr><tr><td>029</td><td>0.9054</td><td>0.9069</td><td>0.9127</td></tr><tr><td>030</td><td>0.9038</td><td>0.9044</td><td>0.9081</td></tr><tr><td>031</td><td>0.8875</td><td>0.8886</td><td>0.8947</td></tr><tr><td>032</td><td>0.8804</td><td>0.8820</td><td>0.8892</td></tr><tr><td>033</td><td>0.8895</td><td>0.8907</td><td>0.8948</td></tr><tr><td>034</td><td>0.9000</td><td>0.9015</td><td>0.9069</td></tr><tr><td>035</td><td>0.8933</td><td>0.8949</td><td>0.9008</td></tr><tr><td>036</td><td>0.8768</td><td>0.8783</td><td>0.8850</td></tr><tr><td>037</td><td>0.8922</td><td>0.8937</td><td>0.8998</td></tr><tr><td>038</td><td>0.9041</td><td>0.9046</td><td>0.9084</td></tr><tr><td>039</td><td>0.9124</td><td>0.9137</td><td>0.9215</td></tr><tr><td>040</td><td>0.8628</td><td>0.8647</td><td>0.8721</td></tr><tr><td>041</td><td>0.8985</td><td>0.9000</td><td>0.9049</td></tr><tr><td>042</td><td>0.8457</td><td>0.8468</td><td>0.8519</td></tr><tr><td>043</td><td>0.9134</td><td>0.9139</td><td>0.9159</td></tr><tr><td>044</td><td>0.8852</td><td>0.8871</td><td>0.8940</td></tr><tr><td>045</td><td>0.9143</td><td>0.9154</td><td>0.9198</td></tr><tr><td>046</td><td>0.9039</td><td>0.9044</td><td>0.9067</td></tr><tr><td>047</td><td>0.9018</td><td>0.9033</td><td>0.9074</td></tr><tr><td>048</td><td>0.8986</td><td>0.9001</td><td>0.9060</td></tr><tr><td>049</td><td>0.9043</td><td>0.9058</td><td>0.9122</td></tr><tr><td>050</td><td>0.9184</td><td>0.9193</td><td>0.9226</td></tr><tr><td>Average</td><td>0.8868</td><td>0.8884</td><td>0.8950</td></tr></tbody></table>

Robustness under noise attacks (salt-and-pepper, Gaussian, speckle).

<table><caption>**Table 4**</caption><thead><tr><th>Attack</th><th>Noise density</th><th>α=0.001</th><th>α=0.01</th><th>α=0.05</th></tr></thead><tbody><tr><td rowspan="3">Salt-and-pepper</td><td>0.03</td><td>0.9248</td><td>0.9206</td><td>0.9156</td></tr><tr><td>0.05</td><td>0.9253</td><td>0.9246</td><td>0.9174</td></tr><tr><td>0.07</td><td>0.9270</td><td>0.9264</td><td>0.9219</td></tr><tr><td rowspan="3">Gaussian</td><td>0.03</td><td>0.9338</td><td>0.9344</td><td>0.9286</td></tr><tr><td>0.05</td><td>0.9375</td><td>0.9363</td><td>0.9318</td></tr><tr><td>0.07</td><td>0.9426</td><td>0.9392</td><td>0.9194</td></tr><tr><td rowspan="3">Speckle</td><td>0.03</td><td>0.9428</td><td>0.9428</td><td>0.9431</td></tr><tr><td>0.05</td><td>0.9428</td><td>0.9426</td><td>0.9432</td></tr><tr><td>0.07</td><td>0.9426</td><td>0.9419</td><td>0.9435</td></tr></tbody></table>

Robustness under image processing attacks.

<table><caption>**Table 5**</caption><thead><tr><th>Attack</th><th>Parameter</th><th>α=0.001</th><th>α=0.01</th><th>α=0.05</th></tr></thead><tbody><tr><td rowspan="2">Winner</td><td>[3 3]</td><td>0.9429</td><td>0.9433</td><td>0.9435</td></tr><tr><td>[5 5]</td><td>0.9432</td><td>0.9431</td><td>0.9435</td></tr><tr><td rowspan="2">Median</td><td>[3 3]</td><td>0.9430</td><td>0.9433</td><td>0.9436</td></tr><tr><td>[5 5]</td><td>0.9433</td><td>0.9435</td><td>0.9432</td></tr><tr><td>Histogram equalization</td><td>-</td><td>0.7745</td><td>0.7613</td><td>0.7498</td></tr><tr><td>Sharpening</td><td>-</td><td>0.9427</td><td>0.9428</td><td>0.9434</td></tr></tbody></table>

Execution time for embedding and extraction (in seconds).

<table><thead><tr><th>Operation</th><th>α=0.001</th><th>α=0.01</th><th>α=0.05</th></tr></thead><tbody><tr><td>Embedding time</td><td>0.32024</td><td>0.29688</td><td>0.30370</td></tr><tr><td>Extracting time</td><td>2.00660</td><td>2.21330</td><td>1.90230</td></tr></tbody></table>

of watermark extraction is quantified using the NC metric, and the results—illustrated in Fig. 9 and Table 2—demonstrate the method’s strong resilience.

**Third**, the watermark used in this method is composed of four sub-watermark images: the QR code of the MITR, the patient’s photograph, and the logos of both the sender and receiver hospitals. These components, each sized at 64 × 64 pixels, are merged to form a final watermark. The integration of high-capacity information while preserving image quality addresses the classic trade-off between capacity, imperceptibility, and robustness. Encoding the MITR document as a QR code facilitates compact, secure embedding, ensuring both data protection and visual integrity.

**Fourth**, computational efficiency is essential in real-world applications, especially in medical settings. Table 5 presents the embedding and extraction times for different scaling factors, showing that the method remains fast and practical. Execution time depends on both algorithmic complexity and the computing platform used. Implementing the algorithm on high-performance systems is expected to yield faster results and minimize any observed artifacts.

**Fifth**, embedding the entire EPR in the form of a test report into the MRI image bit-by-bit would risk compromising image quality and diagnostic accuracy. Transforming this report into a QR code solves this problem, offering (i) MITR Secure Encoding and (ii) substantial capacity reduction by mapping large documents to compact binary images.

**Finally**, a comparative analysis with existing watermarking techniques is presented in Tables 6 and 7. The proposed method demonstrates superior performance across multiple metrics, including imperceptibility, capacity, and robustness. The use of region-based embedding (targeting RONI), QR code encryption, and DWT-SVD integration makes this method highly suitable for medical image transmission and e-health applications aimed at enhancing EPR security.

All these results reinforce the efficiency of the proposed method. However, for real-world applications, a formal reader study should be conducted involving radiologists to evaluate potential perceptual differences or diagnostic errors introduced by the watermarking process.

The proposed method offers several key advantages, including high payload capacity, strong imperceptibility, and low execution time, making it efficient for practical applications. By targeting the Region of Interest (ROI), the approach preserves the diagnostic quality of MRI images while securely embedding sensitive information. It ensures both technical effectiveness and regulatory compliance in handling medical data. However, the method has some limitations, particularly its computational demands on low-end hardware. Additionally, the QR code generation process requires further study to ensure robustness and consistency across various imaging conditions.
<caption>**Table 6**</caption>

A comparative analysis of existing medical watermarking techniques, highlighting key aspects such as employed methods, dataset types, segmentation approaches, and the strengths of each method.

<table><caption>**Table 7**</caption><thead><tr><th>Ref.</th><th>Year</th><th>Description</th><th>Dataset</th><th>Segmentation</th><th>Region-based</th><th>Technique</th><th>Reversible?</th><th>Strengths</th></tr></thead><tbody><tr><td>[16]</td><td>2022</td><td>Robust and secure watermarking technique</td><td>MRI and CT scan images</td><td>No</td><td>Whole image</td><td>DWT, DCT</td><td>Yes</td><td>Security, Robustness</td></tr><tr><td>[11]</td><td>2019</td><td>Robust reversible watermarking method</td><td>MRI, CT, US and other images</td><td>Yes</td><td>Whole image</td><td>IWT, BTC, SVD</td><td>Yes</td><td>Authenticity, Integrity</td></tr><tr><td>[34]</td><td>2022</td><td>Reversible medical image watermarking</td><td>CT Scan</td><td>Yes</td><td>ROI, RONI</td><td>Prediction error expanding, LSB</td><td>Yes</td><td>Authentication, Copyright protection</td></tr><tr><td>[7]</td><td>2021</td><td>Multiple watermarking for health record security</td><td>MRI image</td><td>No</td><td>Whole image</td><td>DTCWT, SVD</td><td>Yes</td><td>Security</td></tr><tr><td>[25]</td><td>2022</td><td>Dual robust watermarking</td><td>CT scan</td><td>Yes</td><td>Whole image</td><td>DCT, QIM-DM</td><td>Yes</td><td>Robustness, Imperceptibility</td></tr><tr><td>[27]</td><td>2023</td><td>Region-based hybrid watermarking method</td><td>MRI, CT, US, Doppler, X-Ray, Mammographic images</td><td>Yes</td><td>ROI, RONI</td><td>DWT, SVD</td><td>Yes</td><td>Imperceptibility, Robustness, Security, Payload, Tamper detection, Recovery accuracy</td></tr><tr><td>[44]</td><td>2023</td><td>Robust blind watermarking method</td><td>MRI, CT, US, and X-Ray images</td><td>No</td><td>Whole image</td><td>DWT</td><td>Yes</td><td>Imperceptibility, Robustness</td></tr><tr><td>[2]</td><td>2024</td><td>Enhanced security watermarking method</td><td>MRI, CT scan, Ultrasound images</td><td>No</td><td>Whole image</td><td>Radon and Slantlet transforms</td><td>Yes</td><td>Robustness, Integrity, Reversibility</td></tr><tr><td>[10]</td><td>2024</td><td>Region-based watermarking method</td><td>MRI images</td><td>Yes</td><td>RONI</td><td>DWT-SVD</td><td>Yes</td><td>Imperceptibility, Capacity, Robustness</td></tr><tr><td colspan="2"><strong>Proposed</strong></td><td>Region-based reversible watermarking method</td><td>MRI images</td><td>Yes</td><td>RONI</td><td>DWT, SVD</td><td>Yes</td><td>Authenticity, high level of Imperceptibility, Robustness, high payload Capacity</td></tr></tbody></table>

The performance comparison of different methods based on PSNR, SSIM, NC, and BER metrics, illustrating the effectiveness and robustness of the proposed approach.

<table><caption>Original MRI Brain Images</caption><thead><tr><th>Study</th><th>PSNR</th><th>SSIM</th><th>NC</th><th>BER</th></tr></thead><tbody><tr><td>[16]</td><td>51.8332</td><td>-</td><td>1.0000</td><td>0.0000</td></tr><tr><td>[11]</td><td>41.2995</td><td>0.9607</td><td>0.9592</td><td>0.0456</td></tr><tr><td>[34]</td><td>56.3361</td><td>-</td><td>-</td><td>-</td></tr><tr><td>[7]</td><td>46.6291</td><td>0.9974</td><td>0.9942</td><td>0.0000</td></tr><tr><td>[25]</td><td>54.9100</td><td>-</td><td>1.0000</td><td>0.0000</td></tr><tr><td>[27]</td><td>43.3000</td><td>0.9900</td><td>0.9999</td><td>0.0000</td></tr><tr><td>[44]</td><td>49.4800</td><td>-</td><td>-</td><td>-</td></tr><tr><td>[2]</td><td>54.18</td><td>0.9878</td><td>0.9661</td><td>0.0020</td></tr><tr><td>[10]</td><td>55.2132</td><td>0.9979</td><td>0.9999</td><td>-</td></tr><tr><td><strong>Proposed</strong></td><td>67.9025</td><td>1.0000</td><td>0.9020</td><td>0.1120</td></tr></tbody></table>

<figure><img src="image_9.png" /><figcaption>Extracted watermark</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 9. Extracted watermarks from selected images without attacks.</figcaption></figure>

## 7. Conclusion

High payload capacity, if not carefully managed, can compromise the imperceptibility and security of watermarked images. This paper proposes a region-based watermarking method for brain MRI images to securely embed medical image test reports, including patient details,

a patient photograph, and sender and receiver hospital logos. By converting the MITR into a QR code image, the method ensures a fixed watermark size regardless of the original report length. This approach addresses the critical trade-off between capacity, imperceptibility, and robustness.

The method demonstrated excellent visual quality, retained diagnostic information in the ROI, and provided strong resilience against a range of attacks. These results confirm that encoding large textual data as a compact QR code enables secure, high-capacity watermarking without compromising visual quality. The proposed method is efficient, reversible, and suitable for secure EPR transmission in medical imaging workflows. Future extensions may adapt this framework for other medical image modalities.

### CRediT authorship contribution statement

**Aicha Benyoucef:** Writing – original draft, Visualization, Validation, Software, Data curation, Conceptualization. **Aya Goudjil:** Writing – review & editing, Software, Investigation, Formal analysis. **M'Hamed Hamadouche:** Writing – review & editing, Validation, Supervision. **Mohammed Chaker Boutalbi:** Writing – review & editing, Visualization, Formal analysis. **Mohammed Ammar:** Writing – review & editing, Validation, Methodology, Investigation. **Mostafa El Habib Daho:** Writing – review & editing, Validation, Supervision, Project administration.

### Declaration of generative AI in scientific writing

During the preparation of this work, the author(s) used ChatGPT to improve readability and language. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Data availability

Data will be made available on request.

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