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Transcript of Thesis Synopsis
Pir Mehr Ali Shah
Arid Agriculture University Rawalpindi
Synopsis for MS (CS) Degree in Computer Science
TITLE: EMPIRICAL ANALYSIS OF DIGITAL IMAGE WATERMARKING
TECHNIQUES ON MEDICAL IMAGES
Name of Student: Saddam Hussain
Registration Number: 02-arid-857
Date of Admission: October 2009
Date of Initiation: 21st June, 2010
Probable Duration: One year
SUPERVISORY COMMITTEE
i) Supervisor Dr. Ayyaz Hussain
ii) Member Mr. Muhammad Amjad Iqbal
iii) Member
Director, Director,
University Institute of Information Technology Advanced Studies
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ABSTRACT
Digital watermarking is the process of embedding information into a digital image
in such a way that it is difficult to remove. The aim of watermarking is to include
subliminal information in a multimedia document to ensure a security service or simply a
labeling application. It would be then possible to recover the embedded message at any
time, even if the document was altered by one or more non-destructive attacks, whether
malicious or not. An application of watermarking is in copyright protection systems,
which are intended to prevent or deter unauthorized copying of digital media.
Watermarking in medical images is a new area of research and some works in this area
have been reported worldwide recently. Most of the works are on the tamper detection of
the images and embedding of the Electronics Patient Record (EPR) data in the medical
images. Watermarked medical images can be used transmission, storage or telediagnosis.
EPR data hiding in images improves the confidentiality of the patient data, saves memory
storage space and reduce the bandwidth requirement for transmission of images. I will
present the impact of various watermarking techniques on medical images.
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INTRODUCTION
Digital watermarking is the process of embedding information into a digital signal
in a way that is difficult to remove. In visible watermarking, the information is visible in
the picture or video. Typically, the information is text or a logo which identifies the
owner of the media. In invisible watermarking, information is added as digital data to
audio, picture or video, but it cannot be perceived as such (although it may be possible
to detect that some amount of information is hidden). The watermark may be intended for
widespread use and is thus made easy to retrieve or it may be a form of steganography,
where a party communicates a secret message embedded in the digital signal. The use of
the word of watermarking is derived from the much older notion of placing a
visible watermark on paper.
The information to be embedded is called a digital watermark, although in some
contexts the phrase digital watermark means the difference between the watermarked
signal and the cover signal. The signal where the watermark is to be embedded is called
the host signal. A watermarking system is usually divided into three distinct steps,
embedding, attack and detection. In embedding, an algorithm accepts the host and the
data to be embedded and produces a watermarked signal.
Watermarking patient data in the medical image has become an interesting topic
recently among the researchers. Though the watermarking is originally proposed for
authentication of the images, the technology is adapted for hiding the EPR in it. Almost
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all the earlier works in medical image watermarking focused mainly on two areas; 1.
tamper detection and authentication and 2. embedding EPR in medical images. Tamper
detection watermarks are used for the probable manipulations done by the hostile people.
Embedding of EPR in medical images will save storage space of the Hospital Information
System (HIS), enhance confidentiality of the patient data, avoid detachment of the
Electronic Patient Record (EPR) data from the image and save bandwidth for
transmission [14, 15, 16, 17].
Authentication, integration and confidentiality are the most important issues
concerned with EPR data exchange through internet [18, 19]. All these requirements can
be achieved using suitable watermarks. The three requirements of general watermarks
(robustness, imperceptibility and capacity) are of specific importance to medical images
also. Since the medical images have region of interest (ROI), achieving the above
requirements without adversely affecting the ROI is a real challenge to the researchers.
Coatrieux et al [20] asserts the relevance of the watermarking in medical images.
Though Piva et al [21] made a general analysis of watermarking techniques in medical
imaging, they have not done an exhaustive search and discussion on different algorithms
presented recently. This paper makes a search on different works done in MIW context. It
will be of immense use for the researchers to understand the state of the art technology in
this field.
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REVIEW OF LITERATURE
The early work on digital image copyright protection has focused on the creation
of a secure and robust watermark only. These works are mainly concerned about the
algorithmic watermark issues and they only touch the deployment problems marginally.
Available digital watermarking techniques can be categorized into one of the two
domains, viz., spatial and transform, according to the embedding domain of the host
image [2]. Least Significant Substitution (LSB) is a simplest technique in the spatial
domain [3,4]. In LSB technique, the watermark is embedded by replacing the least
significant bits of the image data with a bit of the watermark data. There are many
variants of this technique. The data hiding capacity of these algorithms is high. However,
these algorithms are hardly robust for various attacks and prone to tamper by
unauthorized users.
Correlation based approach [2, 5] is another spatial domain technique in which
the watermark is converted to a PN sequence which is then weighted & added to the host
image with a gain factor k. For detection, the watermark image is correlated with the
watermark image. Watermarking in transform domain is more secure and robust to
various attacks.
However, the size of the watermark that can be embedded is generally 1/16 of the
host image. Image watermarking algorithms using Discrete Cosine Transform (DCT)
[6,7], Discrete Wavelet Transform (DWT) [8,9,10], Singular Value Decomposition
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(SVD) [11] are available in the literature. The basic philosophy in majority of the
transform domain watermarking schemes is to modify transform coefficients based on the
bits in watermark image. Most of the domain transformation watermarking schemes
works with DCT and DWT. However Singular Value Decomposition (SVD) is one of the
most powerful numerical analysis techniques and used in various applications [12,13].
There are two major issues with Chang et al.’s method. The first one is, the
watermark extraction is not complete. The error rate between the original watermark and
extracted watermark is not zero. It is very close to zero. That means, the Normalized
correlation coefficient is not ‘1’. If perfect extraction is required, robustness has to be
sacrificed. Both robustness and perfect extraction (zero error rate) cannot be achieved
simultaneously.
The second issue is in the process of complex block selection. A block is said to
be a complex block if the block’s diagonal matrix contains more number of non zero
coefficients. It has been observed that for majority of the blocks, the number of non-zero
coefficients is same. So, it is difficult to identify a block as complex block based on the
number of non-zero coefficients in the diagonal matrix of the block in the host image.
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MATERIALS AND METHODS
A watermarking method is referred to as spread-spectrum if the marked signal is
obtained by an additive modification. Spread-spectrum watermarks are known to be
modestly robust, but also to have a low information capacity due to host interference. A
watermarking method is said to be of quantization type if the marked signal is obtained
by quantization. Quantization watermarks suffer from low robustness, but have a high
information capacity due to rejection of host interference. A watermarking method is
referred to as amplitude modulation if the marked signal is embedded by additive
modification which is similar to spread spectrum method but is particularly embedded in
the spatial domain.
We will be introducing a new adaptive scheme for the impact of different
watermarking techniques on medical images. The goal of the proposed scheme is not
only to find the impact of the different watermarking techniques on medical images but
also to find the comparison of the impact of the techniques of watermarking. The
algorithm or the techniques that are to be used can be classified into two. 1) tamper
detection and authentication and 2) EPR data hiding. Tamper detection watermarks are
able to locate the regions or pixels of the image where tampering was done.
Authentication watermarks are used to identify the source of image. EPR data hiding
techniques gives more importance in hiding high payload data in the images keeping the
imperceptibility very high.
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We also exhort the need of an exclusive benchmarking for MIW. We will discuss
the application of MIW, the advantages and the need of MIW. We will describe the
requirements of MIW, attacks on watermarked images benchmarking requirements and
watermarking algorithms.
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PROPOSED APPROACH
We will be introducing a new adaptive scheme for the impact of different
watermarking techniques on medical images. The aim of the proposed approach is to find
out the impact of different watermarking techniques and algorithms on medical images
and also we will be comparing the impacts of different techniques and algorithms on
medical images.
If a watermarking system is to be used for a particular application, there must be a
standard mechanism for the evaluation of the system Benchmark involves examining a
set of mutually dependent performance factors. But there are no universally accepted
performance measures applicable for every watermarking system. This calls for a
benchmark exclusively for medical image watermarking. In addition to the existing
evaluation parameters(visual quality, robustness, capacity) medical image watermarking
evaluation must include region of interest in the medical image as another parameter. The
robustness of the system must be checked against all the possible transmission and
storage attacks. Rather than performing the evaluation on images of different formats, the
medical image format can be confined to the DICOM standard. The EPR diffusion into
medical images requires more concentration into the capacity of data hiding without
affecting visual quality of the image.
The evaluation of imperceptibility of the mark must consider the properties of
Human Visual System. The security of the system is dependent on the watermarking key
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and the performance evaluation of the system must be done by varying the embedding
strength and different type of keys. The delay encountered during embedding and
recovery of the watermark is also an important factor in telemedicine applications.
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LITERATURE CITED
[1] N.F. Johnson and S.C.Katezenbeisser, A survey of steganographic techniques in
Information Techniques for steganography and digital watermarking, Eds.
Northwood, Artech House, December 1999.
[2] Mohamed Kallel and Mohamed Salim Bouhlel and Jean-Christophe Lapayre
Improved Tian’s Method for Medical Image Reversible Watermarking GVIP
Journal, Volume 7, Issue 2, pp.1-5, 2007.
[3] C.I.Podilchuk and E.J.Delp, Digital Watermarking: Algorithms and Applications,
IEEE Signal Processing Magazine, pp.33-46, July 2001.
[4] I.J.Cox, M.L.Miller, and J.A. Bloom, “Digital Watermarking”, Morgan Kaufmann
Publishers, 2002.
[5] Mehul, S. Raval and Priti P. Scalar Quantization Based Multiple Patterns Data
Hiding Technique for Gray Scale Images, GVIP Journal, Volume 5, Issue 9,
pp.55-61, December 2005.
[6] Barni, F. Bartolini and A. Piva. A DCT domain system for robust image
watermarking. IEEE Transactions on Signal Processing. 66, 357-372, 1998.
[7] W.C.Chu, DCT based image watermarking using sub sampling. IEEE Trans
Multimedia 5, 34-38, 2003.
[8] M.Barni, M., Bartolini, F., V., Piva, A., Improved wavelet based watermarking
through pixel-wise masking. IEEE Trans Image Processing 10, 783- 791, 2001.
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[9] Y. Wang, J.F.Doherty and R.E.Van Dyck, A wavelet based watermarking
algorithm for ownership verification of digital images, IEEE Transactions on
Image Processing, Volume 11, No.2, pp.77-88, February 2002.
[10] Karras D.A. A Second Order Spread Sprectrum Modulation Scheme for Wavelet
Based Low Error Probability Digital Image Watermarking., GVIP Journal,
Volume 5, Issue 3, February 2005.
[11] Chin-Chen Chang, Piyu Tsai and Chia-Chen Lin, 2005 SVD based digital image
watermarking scheme. Pattern Recognition Letters 26, 1577- 1586, 2005.
[12] H.C.Andrews and C.L.Patterson, Singular value Decomposition (SVD) Image
Coding,Hiding IEEE Transactions on Communications 24(4), April 1976, pp.425-
432.
[13] P.Waldemar and T.A.Ramstad, Hybrid KLT-SVD Image Compression, IEEE
International Conf on Acoustics, Speech and Signal Processing, Vol.4, Munich,
Germany, April 21-24, 1997, pp.2713- 2716, 1997.
[14] Ingemar J. Cox, Matthew L. Miller, Jefrey A. Bloom, Digital watermarking,
(Morgan Kaufmann Publishers, 340 Pine Street, Sixth floor, Sans Francisco, CA,
USA, 2004) Image Processing,
[15] Rajendra Acharya U., U. C. Niranjan, S.S. Iyengar, N. Kannathal, Lim Choo Min
“Simultaneous storage of patient information with medical images in the
frequency domain, Computer Methods and Programs in Biomedicine, Vol. 76,
2004, pp.13-19.
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[16] H. Munch, U. Englemann, A. Schroter, H.P. Meinzer “The integration of medical
images with the patient record and their web based distribution” Journal of
Academic Radiolog, 11(6), June 2004, 1995, pp.661-668.
[17] Rajendra Acharya U., P. Subhanna Bhat, Sathish Kumar, Lim Choo Min,
Transmission and storage of medical images with patient information, Journal of
Computers in Biology and Medicine,.33, 2003, pp.303-310.
[18] Hui-Mei chao, Chin-Ming Hsu, Ahaou-Gang Miaou, A data hiding technique
with authentication, integration and confidentiality for electronic patient records,
IEEE trans. Inf. Tech. in biomedicine, 6(1), March 2002, 46-53.
[19] Dan Yu, Farook Sathar, Kai-Kuang Ma Watermark detection and extraction using
independent component analysis, EURASIP J. on Applied Signal Processing
2002, 92-104.
[20] Coatrieux G, H. Maitre, B. Sankur, Y. Rolland, R. collorec, Relevance of
watermarking in medical imaging, Proc. IEEE EMBS int. conf. on Inf. Tech.
applications in Bio-medicine, 2000,250-255.
[21] Alessandro Piva, Franco Bartolini, Iuve Coppini, Alessia De Rosa, Elena
Tamburini, Analysis of data hiding technologies for medical images, Proceedings
of SPIE-IS&T Electronic Imaging, SPIE Vol. 5020 (2003).
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