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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