Robust Watermarking Scheme for NIfTI Medical Images

Kumar, Abhishek; Singh, Kamred Udham; Kumar, V. D. Ambeth; Kant, Tapan; Saudagar, Abdul Khader Jilani; Tameem, Abdullah Al; Khathami, Mohammed Al; Khan, Muhammad Badruddin; Hasanat, Mozaherul Hoque Abul; Malik, Khalid Mahmood

doi:10.32604/cmc.2022.022817

Cited by 8 publications

(10 citation statements)

References 23 publications

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“…The comparison reveals that the results obtained using our procedure were far more substantial than those obtained using existing strategies. As shown in Table 2, in terms of PSNR, our technique outperformed the methods of Kumar et al [1], Singh et al [2], Sun et al [17], Kang et al [21], Rayachoti et al [24], Rasha et al [25], and Bamal et al [26] and also showed significant PSNRs. Our approach's SSIM is closer to one than other existing techniques, demonstrating the improved quality of the watermarked slice produced by our methodology.…”

Section: Comparison With Existing Schemesmentioning

confidence: 63%

“…viz. Kumar et al [1], Singh et al [2], and Sun et al [17]. In some cases, our method approaches the maximum normalized correlation.…”

Section: Comparison With Existing Schemesmentioning

confidence: 89%

“…Diagnosis may be made by connecting these medical modalities to radiologists over the internet, generating medical images in various formats viz. DICOM, ANALYZE, NIfTI, NRRD, and MINC [1]. Doctors receive diagnosis results that are far faster and much more accurate than film-based imaging methods.…”

Section: Introductionmentioning

confidence: 99%

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Secure NIfTI Image Authentication Scheme for Modern Healthcare System

et al. 2023

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Advances in digital neuroimaging technologies, i.e., MRI and CT scan technology, have radically changed illness diagnosis in the global healthcare system. Digital imaging technologies produce NIfTI images after scanning the patient’s body. COVID-19 spared on a worldwide effort to detect the lung infection. CT scans have been performed on billions of COVID-19 patients in recent years, resulting in a massive amount of NIfTI images being produced and communicated over the internet for diagnosis. The dissemination of these medical photographs over the internet has resulted in a significant problem for the healthcare system to maintain its integrity, protect its intellectual property rights, and address other ethical considerations. Another significant issue is how radiologists recognize tempered medical images, sometimes leading to the wrong diagnosis. Thus, the healthcare system requires a robust and reliable watermarking method for these images. Several image watermarking approaches for .jpg, .dcm, .png, .bmp, and other image formats have been developed, but no substantial contribution to NIfTI images (.nii format) has been made. This research suggests a hybrid watermarking method for NIfTI images that employs Slantlet Transform (SLT), Lifting Wavelet Transform (LWT), and Arnold Cat Map. The suggested technique performed well against various attacks. Compared to earlier approaches, the results show that this method is more robust and invisible.

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Section: Comparison With Existing Schemesmentioning

confidence: 63%

“…viz. Kumar et al [1], Singh et al [2], and Sun et al [17]. In some cases, our method approaches the maximum normalized correlation.…”

Section: Comparison With Existing Schemesmentioning

confidence: 89%

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Secure NIfTI Image Authentication Scheme for Modern Healthcare System

et al. 2023

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“…The parameters such as Peak signal-to--to-noise ratio (PSNR) and Normalized Correlation (NC) are useful to assess the effectiveness of the scheme [Najafi and Loukhaoukha 2019], [Singh et al 2022(b)]. PSNR is concerned with evaluating the degradation of dB with the entrenching process in the watermarked images using the following equation.…”

Section: Figure 3: Ultrasound Images Used In Our Experimentsmentioning

confidence: 99%

Color Ultrasound Image Watermarking Scheme Using FRT and Hessenberg Decomposition for Telemedicine Applications

Kumar¹,

Singh²

2022

jucs

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Watermarking is a valuable technique for verifying medical images obtained through the internet for diagnosis. There is a greater need for security in medical pictures with ever- increasing security risks. This research presented a Finite Ridgelet Transform (FRT)-Hessenberg based watermarking scheme in medical images. The suggested paradigm is divided into two stages. Before watermark insertion, FRT is applied to medical images. The coefficients are combined into blocks of 4 x 4 and each block is decomposed using Hessenberg decomposition. The second column of the Q matrix is used to insert the watermark using the additive quantization technique. The results obtained from our experiment have given good visual quality of the watermarked images. The high PSNR value 53.6121 and NC value 1.0 show that our scheme is performing better. Moreover, the performance of our scheme is robust against several attacks. The consequences of this result imply that the anticipated scheme is effective for medical image watermarking.

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“…Before the watermark embedding, it is also subject to LU factorization, which creates the binary vector and inserts the LL band of the MSVD. Kumar et al [13] propose a scheme using LWT and QR factorization for NIfTI images. The authors initially extracted the patient metadata and the slice data from the source image.…”

Section: Introductionmentioning

confidence: 99%

A Robust NIfTI Image Authentication Framework Based on DST and Multi-Scale Otsu Thresholding

et al. 2022

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Telemedicine has been intensely promoted in the present pandemic situation of COVID-19 to maintain a strategic distance from the infected person. Several medical tests were used to detect the coronavirus, including antigen, RT-PCR, and a lung CT scan. Only a lung CT-Scan can detect the coronavirus and provide information about the lung infection. As a result, digital imaging plays a critical role in the current pandemic situation. Teleradiology allows for the communication of digital medical images of patients over the internet for diagnosis. A lung CT-Scan test is currently being performed on billions of people to detect COVID-19. These images were sent via the internet for diagnosis and research purposes. The NIfTI image file (.nii extension) was created by the CT-Scanner and contains multiple slices of the lungs. As a result, radiologists determine that the received image has not been tempered during transmission, posing a critical authenticity problem when transmitting these images over the internet. As a result, the researchers are more concerned about the integrity and authenticity of these images in teleradiology. This paper proposes a blind, robust watermarking scheme for lung CT-Scan NIfTI images to address this issue. We use Otsu's image segmentation algorithm in the proposed scheme to identify the slice with the least amount of medical information for watermark embedding. The proposed scheme employs the Discrete Shearlet Transform (DST), Lifting Wavelet Transform (LWT), and Schur decomposition to embed the encrypted watermark. Watermarks are encrypted using the Affine Transform. The experimental results show that watermarked slice has been tainted by the addition of various sorts of noise, including salt-and-pepper noise, compression, Gaussian noise, speckle noise, and motion blur. After an attack, a watermark is retrieved, and the NC values of extracted watermarks are 0.99623 for Salt and pepper noise, 0.96964 for Gaussian noise, 0.99014 for Speckle noise. The proposed scheme was put to the test with a variety of attacks and produced significant results.

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Robust Watermarking Scheme for NIfTI Medical Images

Cited by 8 publications

References 23 publications

Secure NIfTI Image Authentication Scheme for Modern Healthcare System

Secure NIfTI Image Authentication Scheme for Modern Healthcare System

Color Ultrasound Image Watermarking Scheme Using FRT and Hessenberg Decomposition for Telemedicine Applications

A Robust NIfTI Image Authentication Framework Based on DST and Multi-Scale Otsu Thresholding

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