1. Multiple Image Watermarking Applied to Health Information Management
Reporter :黃阡廷

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

3. introduction
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

4. introduction Research Method: -quantization function
-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)

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

6. Proposed Method
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

7. Algorithm
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

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

9. Algorithm 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.

11. Selection of Embeddable Coefficients
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

12. 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

13. Allocation of Watermarks According to Robustness and Capacity Criteria

14. Results
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

15. BCH 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

16. PSNR & wPSNR
(a) Original image. (b) Resulting watermarked image.

17. 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

18. PSNR & 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.

19. PSNR & wPSNR

20. NHD w : the original watermark Normalized Hamming Distance (NHD)
w hat : extracted fragile watermark
Nw : the length of the watermark

21. Percentage of Error Bits in Extracted Watermarks

22. (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

23. Conclusions
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.