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Multiple Image Watermarking Multiple Image Watermarking Applied to Health Information Applied to Health Information

ManagementManagement Reporter :黃阡廷

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OutlineOutline

Introduction Proposed Method Algorithm Selection of Embeddable Coefficients Results BCH encoding PSNR & wPSNR NHD Conclusions

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introductionintroduction IEEE Transactions on Information Technology in

Biomedicine, VOL. 10, NO. 4, October 2006

Author: Aggeliki Giakoumaki, Sotiris Pavlopoulos, Dimitris Koutsouri

Research Motivation and Background: -huge and exponentially increasing amount of medical data -sensitive nature of patients’ personal data

Research Purpose: -potentials of digital watermarking in medical data management

issues

-proposes a multiple watermarking scheme regarding health data handling

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introductionintroduction Research Method: -Haar wavelet transform -quantization function -multiple watermarks embedding procedure data watermarks: signature, index, caption

watermark -energy of approximation -BCH encoding schemes S. Zinger, Z. Jin, H. Maitre, and B. Sankur, “Optimization of

Watermarking performances using error correcting codes and repetition”

-peak signal-to-noise ratio (PSNR) -weighted PSNR (wPSNR) -noise visibility function (NVF) -normalized hamming distance (NHD)

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introductionintroduction

regions of interest (ROI) Medical Data Management Issues: -Access Control -De-identification -Captioning -Origin Identification -Integrity Control -Indexing

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PProposedroposed M Methodethod

dyadic scaling decomposition of the wavelet transform and the signal processing of the human visual system (HVS)

signature watermark: source authentication by the recipient

index watermark: image retrieval by database querying mechanisms

caption watermark: additional data useful for the diagnosis

reference watermark: data integrity control and tampering localization

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AlgorithmAlgorithm

Haar wavelet transform produces coefficients that are dyadic rational numbers: 2l

quantization function:

-k is an integer

-s is a user-defined offset for increased security -Δ, the quantization parameter, is a positive real number

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AlgorithmAlgorithm

quantization parameter Δ is defined as: Δ = 2l , where l is the decomposition level.

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AlgorithmAlgorithm

The multiple watermarks embedding procedure: Step 1: four-level Haar wavelet decomposition

Step 2: watermark bit wi is embedded into the coefficient

f according to the following: a) If Q (f ) = wi , the coefficient is not modified.

b) following assignment:

Q (f ) =wi

Step 3: The watermarked image is produced by the corresponding

four-level inverse wavelet transform.

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Selection of Embeddable Selection of Embeddable CoefficientsCoefficients

signature watermark is embedded in the fourth decomposition level

index watermark is embedded in the third decomposition level

caption watermark is embedded in the second decomposition level

the first decomposition level is used for fragile watermarking to allow data integrity control

reference watermark is embedded in selected coefficients of the other three decomposition levels

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Energy of Approximation and Detail Images of a Four-Level Wavelet Decomposition

k denotes the approximation and the detail images at each of the decomposition levels

Ik are the coefficients of the subband images

Nk and Mk are their corresponding dimensions

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Allocation of Watermarks According to Robustness and Capacity Criteria

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ResultsResults

test set consisted of 50 ultrasound images of size 256×320 pixels

signature watermark containing the doctor’s identification key is a 128-b watermark

reference watermark is a binary array index and caption watermarks are binary arrays

produced by the ASCII codes of text files set of keywords consisted of six words and a total

of 52 characters patient’s data comprised of 23 words, of 208

characters in total

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BCH encodingBCH encoding

In order to increase robustness of the embedded data (signature, index, caption), error correction coding was implemented.

BCH encoding schemes: BCH(n, k, l ) n : codeword of length

k : bits of the watermark array l : can correct bit errors

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PSNR & wPSNRPSNR & wPSNR

(a) Original image. (b) Resulting watermarked image.

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PSNR & wPSNRPSNR & wPSNR

I : original image I hat : watermarked image N I : the number of pixels in the image

maxI (m, n): the maximum gray value of the original image

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PSNR & wPSNRPSNR & wPSNR

The weighted PSNR (wPSNR) is a quality metric that assigns different weights to the perceptually different image regions, based on the noise visibility function (NVF).

For flat regions, the NVF value is close to 1, whereas for edge or textured regions, it is closer to 0.

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PSNR & wPSNRPSNR & wPSNR

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NHDNHD

Normalized Hamming Distance (NHD)

w : the original watermark

w hat : extracted fragile watermark Nw : the length of the watermark

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Percentage of Error Bits in Extracted Watermarks

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(a) Ultrasound image with a blurred region.

(b) Tampering detection through the difference image of the 1st decomposition level reference watermark. method has been tested on other medical imaging modalities namely MRA, CT, MRI, and PET, and the results were also satisfactory

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ConclusionsConclusions

Digital watermarking has the potential to provide complementary and alternative solutions in a range of issues of critical importance to health informatics.

The experimental results demonstrate the efficiency of the scheme, which could be extended and integrated into healthcare

information systems.

Future work involves integration of the watermarking scheme with JPEG2000 compression.