A Novel Fractional‐Discrete‐Cosine‐Transform‐Based Reversible Watermarking for Healthcare Information Management Systems

Ko, Lu Ting; Chen, Jwu E.; Shieh, Yaw Shih; Scalia, Massimo; Sung, Tze Yun

doi:10.1155/2012/757018

Cited by 15 publications

(7 citation statements)

References 26 publications

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“…Reversible watermarking approaches can be categorized into four groups: compression based [10,11], histogram modification based [12][13][14][15], quantization based [16][17][18][19], and Difference Expansion (DE) based [20][21][22]. Recently, reversible watermarking based on the DE technique has been suggested in many kinds of studies, and they typically exceed the other types of reversible methods in that they offer higher payload capacity and lower complexity compared to the other methods [23][24][25][26].…”

Section: Reversibilitymentioning

confidence: 99%

Assessment of perceptual distortion boundary through applying reversible watermarking to brain MR images

Qasim

Aspin

Meziane

et al. 2019

Signal Processing: Image Communication

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The digital medical workflow faces many circumstances in which the images can be manipulated during viewing, extracting and exchanging. Reversible and imperceptible watermarking approaches have the potential to enhance trust within the medical imaging pipeline through ensuring the authenticity and integrity of the images to confirm that the changes can be detected and tracked. This study concentrates on the imperceptibility issue. Unlike reversibility, for which an objective assessment can be easily made, imperceptibility is a factor of human cognition that needs to be evaluated within the human context. By defining a perceptual boundary of detecting the modification, this study enables the formation of objective guidelines for the method of data encoding and level of image/pixel modification that translates to a specific watermark magnitude. This study implements a relative Visual Grading Analysis (VGA) evaluation of 117 brain MR images (8 original and 109 watermarked), modified by varying techniques and magnitude of image/pixel modification to determine where this perceptual boundary exists and relate the point at which change becomes noticeable to the objective measures of the image fidelity evaluation. The outcomes of the visual assessment were linked to the images Peak Signal to Noise Ratio (PSNR) values, thereby identifying the visual degradation threshold. The results suggest that, for watermarking applications, if a watermark is applied to the 512x512 pixel (16 bpp grayscale) images used in the study, a subsequent assessment of PSNR=82dB or greater would mean that there would be no reason to suspect that the watermark would be visually detectable.

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Section: Reversibilitymentioning

confidence: 99%

Assessment of perceptual distortion boundary through applying reversible watermarking to brain MR images

Qasim

Aspin

Meziane

et al. 2019

Signal Processing: Image Communication

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“…On the whole, EBS will improve embedding capacity shown in Figures 5-8. In addition, taking a comparison with (17) by (18), (19), and (20), the inequation denoted by (21) comes into being. So, it can be concluded that expansion bins selecting strategy contributes to bringing down embedding distortion.…”

Section: Bit Extraction and Pixel Recoverymentioning

confidence: 99%

“…Three categories of solutions can be summarized to this problem, difference expansion (DE) [8][9][10], histogram shifting (HS) [11][12][13][14], and integer transformation [15][16][17][18][19]. Difference expansion computes and expands the differences between pixels rather than the pixel itself to embed data.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO

Xiang

Yuan

Hou

2016

Mathematical Problems in Engineering

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Most pixel-value-ordering (PVO) predictors generated prediction-errors including −1 and 1 in a block-by-block manner. Pixel-based PVO (PPVO) method provided a novel pixel scan strategy in a pixel-by-pixel way. Prediction-error bin 0 is expanded for embedding with the help of equalizing context pixels for prediction. In this paper, a PPVO-based hybrid predictor (HPPVO) is proposed as an extension. HPPVO predicts pixel in both positive and negative orientations. Assisted by expansion bins selection technique, this hybrid predictor presents an optimized prediction-error expansion strategy including bin 0. Furthermore, a novel field-biased context pixel selection is already developed, with which detailed correlations of around pixels are better exploited more than equalizing scheme merely. Experiment results show that the proposed HPPVO improves embedding capacity and enhances marked image fidelity. It also outperforms some other state-of-the-art methods of reversible data hiding, especially for moderate and large payloads.

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“…Therefore, fully reversible watermarking techniques, which can completely recover both the original unmodified image and the embedded watermark, are developed. Reversible watermarking approaches can be categorized into four groups: compression based [7,10], histogram modification based [16,21,33], quantization based [22,23] and Difference Expansion (DE) based [20,28]. Reversible watermarking based on the DE technique are recommended by many recent studies, and typically exceed alternate reversible methods in terms of higher payload capacity and lower complexity [20,28,41].…”

Section: Introductionmentioning

confidence: 99%

ROI-based reversible watermarking scheme for ensuring the integrity and authenticity of DICOM MR images

Qasim

Aspin

Meziane

et al. 2018

Multimed Tools Appl

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Reversible and imperceptible watermarking is recognized as a robust approach to confirm the integrity and authenticity of medical images and to verify that alterations can be detected and tracked back. In this paper, a novel blind reversible watermarking approach is presented to detect intentional and unintentional changes within brain Magnetic Resonance (MR) images. The scheme segments images into two parts; the Region of Interest (ROI) and the Region of Non Interest (RONI). Watermark data is encoded into the ROI using reversible watermarking based on the Difference Expansion (DE) technique. Experimental results show that the proposed method, whilst fully reversible, can also realize a watermarked image with low degradation for reasonable and controllable embedding capacity. This is fulfilled by concealing the data into 'smooth' regions inside the ROI and through the elimination of the large location map required for extracting the watermark and retrieving the original image. Our scheme delivers highly imperceptible watermarked images, at 92.18-99.94 dB Peak Signal to Noise Ratio (PSNR) evaluated through implementing a clinical trial based on relative Visual Grading Analysis (relative VGA). This trial defines the level of modification that can be applied to medical images without perceptual distortion. This compares favorably to outcomes reported under current state-of-art techniques. Integrity and authenticity of medical images are also ensured through detecting subsequent changes enacted on the watermarked images. This enhanced security measure, therefore, enables the detection of image manipulations, by an imperceptible approach, that may establish increased trust in the digital medical workflow.

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A Novel Fractional‐Discrete‐Cosine‐Transform‐Based Reversible Watermarking for Healthcare Information Management Systems

Cited by 15 publications

References 26 publications

Assessment of perceptual distortion boundary through applying reversible watermarking to brain MR images

Assessment of perceptual distortion boundary through applying reversible watermarking to brain MR images

Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO

ROI-based reversible watermarking scheme for ensuring the integrity and authenticity of DICOM MR images

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