A Study of DWT and SVD Based Watermarking Algorithms for Patient Privacy in Medical Images

Mehta, Sachin; Nallusamy, Rajarathnam; Marawar, Ranjeet Vinayak; Prabhakaran, Balakrishnan

doi:10.1109/ichi.2013.41

Cited by 17 publications

(6 citation statements)

References 14 publications

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“…The Peak Signal to Noise Ratio (PSNR The Peak Signal to Noise Ratio (PSNR): measures the similarity between the original image and the watermarked image [10]:…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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A Robust Hybrid Watermarking Technique for Securing Medical Image

Assini¹,

Badri²,

Safi³

et al. 2018

IJIES

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In the aim to contribute to the security of medical image, we present a robust watermarking method which combines discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD). This approach is intended to insert an invisible image watermark in a medical image. The cover medical image is divided up into the third level of DWT coefficients and then is transformed by DCT and SVD. The same procedure is applied to the watermark image. The singular value of watermark is inserted into the singular value of the high-frequency sub bands of the third level DWT of the cover image. However, the insertion of the watermark in these areas makes it possible to reinforce the robustness of the system of watermarking without hindering the quality of the watermarked image. The performance of the proposed method was evaluated in term of invisibility by calculating the Peak Signal to Noise Ratio (PSNR) between the original and the watermarked image and in term of robustness by measuring the normalized correlation coefficient (NC) between the original watermark and the extracted watermark after applying attacks. The experimental results approve that our proposed hybrid algorithm gives an excellent compromise imperceptibility and robustness against several attacks such as Gaussian noise, Saltand-Pepper, Speckle noise, Average filter, Median filter, and Wiener filter compared with existing methods.

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“…The Peak Signal to Noise Ratio (PSNR The Peak Signal to Noise Ratio (PSNR): measures the similarity between the original image and the watermarked image [10]:…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Where MSE [10] represents the mean square error to measure the perceptual distance between watermarked and original image. MSE can be defined as [11]: (10) Where I and I' are the original image and the watermarked image of size M x N respectively.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

A Robust Hybrid Watermarking Technique for Securing Medical Image

Assini¹,

Badri²,

Safi³

et al. 2018

IJIES

View full text Add to dashboard Cite

show abstract

“…The PCA-Similarity Factor [25] and Extended Frobenius norm (EROS) [53], which use matrix factorization techniques, such as singular vector decomposition (SVD) and principle component analysis, have also been proposed to transform the input multi-variate time series into equal length and then apply cosine similarity over them. Applications of SVD and DTW for various multimedia tasks, such as similarity search, classification, recognition, and watermarking, include References [19,22,26,32,46].…”

Section: Related Workmentioning

confidence: 99%

Robust Multi-Variate Temporal Features of Multi-Variate Time Series

Poccia

Candan

Sapino

et al. 2018

ACM Trans. Multimedia Comput. Commun. Appl.

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Many applications generate and/or consume multi-variate temporal data, and experts often lack the means to adequately and systematically search for and interpret multi-variate observations. In this article, we first observe that multi-variate time series often carry localized multi-variate temporal features that are robust against noise. We then argue that these multi-variate temporal features can be extracted by simultaneously considering, at multiple scales, temporal characteristics of the time series along with external knowledge, including variate relationships that are known a priori. Relying on these observations, we develop data models and algorithms to detect robust multi-variate temporal (RMT) features that can be indexed for efficient and accurate retrieval and can be used for supporting data exploration and analysis tasks. Experiments confirm that the proposed RMT algorithm is highly effective and efficient in identifying robust multi-scale temporal features of multi-variate time series. CCS Concepts: • Mathematics of computing → Time series analysis; • Information systems → Temporal data; Multimedia databases; Data mining; Proximity search; Multimedia and multimodal retrieval; • Computing methodologies → Activity recognition and understanding; Unsupervised learning; • Theory of computation → Pattern matching;

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“…Others attempt to discover new knowledge or make recommendations through accessing multiple healthcare data sources, while, at the same time, protecting the privacy of patients [Hoens et al 2013;Park and Ghosh 2013]. Techniques that focus on protecting the privacy of video and image data for home-monitoring systems and medical-imaging systems have also been investigated [Mehta et al 2013].…”

Section: Systemsmentioning

confidence: 99%

Smart Health and Wellbeing

Yang

Leroy

Ananiadou

2013

ACM Trans. Manage. Inf. Syst.

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Healthcare informatics has drawn substantial attention in recent years. Current work on healthcare informatics is highly interdisciplinary involving methodologies from computing, engineering, information science, behavior science, management science, social science, as well as many different areas in medicine and public health. Three major tracks, (i) systems, (ii) analytics, and (iii) human factors, can be identified. The systems track focuses on healthcare system architecture, framework, design, engineering, and application; the analytics track emphasizes data/information processing, retrieval, mining, analytics, as well as knowledge discovery; the human factors track targets the understanding of users or context, interface design, and user studies of healthcare applications. In this article, we discuss some of the latest development and introduce several articles selected for this special issue. We envision that the development of computing-oriented healthcare informatics research will continue to grow rapidly. The integration of different disciplines to advance the healthcare and wellbeing of our society will also be accelerated.

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A Study of DWT and SVD Based Watermarking Algorithms for Patient Privacy in Medical Images

Cited by 17 publications

References 14 publications

A Robust Hybrid Watermarking Technique for Securing Medical Image

A Robust Hybrid Watermarking Technique for Securing Medical Image

Robust Multi-Variate Temporal Features of Multi-Variate Time Series

Smart Health and Wellbeing

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