Multiple Image Watermarking Applied to
Health Information Management
Reporter :黃阡廷
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Outline
Introduction
 Proposed Method
 Algorithm
 Selection of Embeddable Coefficients
 Results
 BCH encoding
 PSNR & wPSNR
 NHD
 Conclusions
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introduction
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IEEE Transactions on Information Technology in Biomedicine, VOL.
10, NO. 4, October 2006
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Author: Aggeliki Giakoumaki, Sotiris Pavlopoulos, Dimitris Koutsouri
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Research Motivation and Background:
-huge and exponentially increasing amount of medical data
-sensitive nature of patients’ personal data
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Research Purpose:
-potentials of digital watermarking in medical data management issues
-proposes a multiple watermarking scheme regarding health data handling
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introduction
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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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introduction
regions of interest (ROI)
 Medical Data Management Issues:
-Access Control
-De-identification
-Captioning
-Origin Identification
-Integrity Control
-Indexing
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Proposed Method
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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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Algorithm
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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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Algorithm
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quantization parameter Δ is defined as: Δ = 2l , where
l is the decomposition level.
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Algorithm
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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 Coefficients
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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
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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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Results
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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 encoding
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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 & wPSNR
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(a) Original image. (b) Resulting watermarked image.
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PSNR & 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 & wPSNR
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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 & wPSNR
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NHD
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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.
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(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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Conclusions
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Digital watermarking has the potential to provide complementary
and alternative solutions in a range of issues of critical importance
to health informatics.
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The experimental results demonstrate the efficiency of the scheme,
which could be extended and integrated into healthcare
information systems.
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Future work involves integration of the watermarking scheme with
JPEG2000 compression.
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