A RONI Based Visible Watermarking Approach for Medical Image Authentication

Thanki, Rohit M.; Borra, Surekha; Dwivedi, Ved Vyas; Borisagar, Komal R.

doi:10.1007/s10916-017-0795-3

Cited by 34 publications

(11 citation statements)

References 12 publications

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“…Bit error rate also reduces in this approach by adopting various error correction codes for encryption of patient record. Visible watermarking is done in 34 where RONI and ROI portions are obtained through human visual system; further various spatial operations are done over RONI portion for attaining watermarked image with visible logo. Though, it appears that this scheme achieves low correlation values for visible logo.…”

Section: Related Workmentioning

confidence: 99%

A Secured and Robust Medical Image Watermarking Approach for Protecting Integrity of Medical Images

Khare

Srivastava

2020

Trans Emerging Tel Tech

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In this paper, a novel medical image watermarking (MIW) technique for tele ‐ medicine applications is proposed. In this approach homomorphic transform (HT), redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD) transforms are successfully utilized for developing an effective watermarking technique. Reflectance component of medical host image is obtained using HT, on which RDWT decomposition and SVD are applied. Similarly medical watermark image is processed with RDWT and SVD. Singular values (Svs) were used for embedding the watermark image into host image. 2‐D chaotic Arnold transform (AT) is used for providing extra security to the watermark image by encrypting the watermarked image. Moreover, proposed technique is also tested for several wavelet families. Robustness and imperceptibility of the proposed technique is enhanced as depicted in experimental results under various attacks. Furthermore, better robustness is attained from proposed technique on comparing it with other formerly reported schemes.

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Section: Related Workmentioning

confidence: 99%

A Secured and Robust Medical Image Watermarking Approach for Protecting Integrity of Medical Images

Khare

Srivastava

2020

Trans Emerging Tel Tech

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“…As the currently international compression standard, JPEG compression is widely used in watermarking [1,2,[7][8][9]. In the watermarking field, many scholars used JPEG attacks [8][9][10]13,14,18,19,21,22]. High-intensity JPEG compression may be applied to watermarking images.…”

Section: Gaussian Noise Attacksmentioning

confidence: 99%

“…Therefore, finding a feature vector that can resist geometric attacks in medical images so that the algorithm can show good robustness without changing the original image is a problem that has long puzzled researchers [7,8,12,13,20,21]. For algorithms based on the first and third categories, the embedding of watermarks affects the images themselves, and these watermarking algorithms have poor anti-geometric attack capabilities; they cannot even resist the tiny geometric transformations [9,10,[22][23][24]. For medical images, the particularity is that the embedded watermark cannot affect the diagnosis of doctors, and cannot obviously change the main content of medical images, especially for the region of interest of the image.…”

Section: Introductionmentioning

confidence: 99%

A Robust Multi-Watermarking Algorithm for Medical Images Based on DTCWT-DCT and Henon Map

Liu

et al. 2019

Applied Sciences

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To resolve the contradiction between existing watermarking methods—which are not compatible with the watermark’s ability to resist geometric attacks—and robustness, a robust multi-watermarking algorithm suitable for medical images is proposed. First, the visual feature vector of the medical image was obtained by dual-tree complex wavelet transform and discrete cosine transform (DTCWT-DCT) to perform multi-watermark embedding and extraction. Then, the multi-watermark was preprocessed using the henon map chaotic encryption technology to strengthen the security of watermark information, and combined with the concept of zero watermark to make the watermark able to resist both conventional and geometric attacks. Experimental results show that the proposed algorithm can effectively extract watermark information; it implements zero watermarking and blind extraction. Compared with existing watermark technology, it has good performance in terms of its robustness and resistance to geometric attacks and conventional attacks, especially in geometric attacks.

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“…This has allowed doctors to more intuitively observe whether the tissues or organs represented by the image have lesions, the location and size of the lesions, etc., so as to help them to conduct comprehensive pathological inquiry, technical communication, demonstration and simulated surgery. Most of today's existing medical images are comprised of threedimensional volume data, in particular 80% of the digital images generated by the new generation of medical devices are three-dimensional volume data, such as CT, MRI, ultrasound imaging and PET [4], [6], [20]. Fig.…”

Section: Introductionmentioning

confidence: 99%

“…The research on medical volume data are relatively few, which are mainly divided into three classes, the first of which is 1) Medical image watermarking based on RONI. Considering the sensitivity and intolerable changes in medical image content, watermark embedding information is often chosen to be embedded in the non-region of interest (RONI) [1], [4]- [6]. A medical image region of interest (ROI) refers to a lesion area that contains important pathological features or diagnostic and therapeutic information.…”

Section: Introductionmentioning

confidence: 99%

Robust Watermarking Algorithm for Medical Volume Data in Internet of Medical Things

Liu

et al. 2020

IEEE Access

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The advancement of 5G technology, big data and cloud storage has promoted the rapid development of the Internet of Medical Things (IoMT). Based on the strict security requirements and high level of accuracy required for disease diagnosis and pathological analysis, 3D medical volume data have been created in large numbers. The IoMT facilitates the rapid transfer of medical information and also makes the protection of pathological information and privacy information of patients increasingly prominent. To solve the security problem, a robust zero-watermarking algorithm based on 3D hyperchaos and 3D dualtree complex wavelet transform is proposed according to the selected feature of medical volume data. The feature combines human visual features with improved perceptual hashing techniques. It is a robust and efficient binary sequence. When implementing the proposed algorithm, the watermark is first scrambled with 3D hyperchaos to enhance security. Then, 3D DTCWT-DCT transformation is applied to medical volume data, and the low-frequency coefficients that can represent the features are selected and binarized to generate the secret key to complete the watermark embedding and extraction. Zero embedding and blind extraction ensure that the original medical volume data is not altered in any form, which meets the special requirements for diagnosis. Simulation results show that the algorithm is robust and can effectively resist common attacks and geometric attacks. It used fewer robust features to effectively bound medical volume data and watermark information, saved bandwidth, and satisfied the security of transmission and storage of medical volume data in the Internet of medical things. In particular, compared with state-of-the-art technology, the proposed algorithm improves the average NC value by 46.67% under geometric attacks. INDEX TERMS Hyperchaos, Internet of medical things, medical volume data, robust watermark, 3D-DTCWT-DCT.

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A RONI Based Visible Watermarking Approach for Medical Image Authentication

Cited by 34 publications

References 12 publications

A Secured and Robust Medical Image Watermarking Approach for Protecting Integrity of Medical Images

A Secured and Robust Medical Image Watermarking Approach for Protecting Integrity of Medical Images

A Robust Multi-Watermarking Algorithm for Medical Images Based on DTCWT-DCT and Henon Map

Robust Watermarking Algorithm for Medical Volume Data in Internet of Medical Things

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