Dual Adaptive Watermarking for Biomedical Images

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DUAL ADAPTIVE WATERMARKING SCHEMES FOR

DICOM IMAGES

A PROJECT REPORT

submitted by

MANJARI TYAGI(091237)

PALLAVI JAIN(091310)

TAPAS TRIVEDI(091324)

UNDER THE SUPERVISION OF

DR.SHISHIR KUMAR (HOD CSE)

May-2013

submi tted in par tial fu lf il lment f or the award of the degree of

Bachelor of Te chnology

INDepartment of Computer Science & Engineering

Department of Computer Science & Engineering

JAYPEE UNIVERSITY OF ENGINEERING & TECHNOLOGY, AB

ROAD, RAGHOGARH, DT. GUNA-473226 MP, INDIA



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JAYPEE UNIVERSITY OF ENGINEERING & TECHNOLOGY

A.B. ROAD, P.B. No. 1, RAGHOGARH, DIST: GUNA (M.P.) INDIA.

Phone: 07544 267051, 267310 - 14 Fax: 07544 267011

Website: www.juet.ac.in

CERTIFICATE

This is to certify that the work titled ―Dual Adaptive Watermarking for DICOM

images” submitted by ―Manjari Tyagi (091237) ” , ―Pallavi Jain (091310) ‖, and―Tapas Trivedi(091324) ‖ in partial fulfillment for the award of degree of Bachelor of

Technology in Computer Science of Jaypee University of Engineering & Technology,

Guna has been carried out under my supervision. This work has not been submitted

partially or wholly to any other University or Institute for the award of this or any other

degree or diploma.

Signature of Supervisor

(Dr. Shishir Kumar )

Sr. Lecturer

Date:



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ACKNOWLEDGMENT

We are extremely grateful and remain indebted to our guide Dr. Shishir Kumar(HOD)

for being a source of inspiration and for his constant support in the Design,

Implementation and Evaluation of the project. We are thankful to him for his constant

constructive criticism and invaluable suggestions, which benefited us a lot while

developing the project on ― DUAL ADAPTIVE WATERMARKING FOR

BIOMEDICAL IMAGES” . He has been a constant source of inspiration and

motivation for hard work. He has been very co-operative throughout this project work.

Through this column, it would be our utmost pleasure to express our warm thanks to

him for their encouragement, co-operatio n and consent without which we mightn‘t beable to accomplish this project.

We also express our gratitude to Mr. Puneet Pandey for providing us the

infrastructure to carry out the project and to all staff members who were directly and

indirectly instrument in enabling us to stay committed for the project.

Manjari Tyagi [091237]

Pallavi Jain [091310]

Tapas Trivedi [091324]



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EXECUTIVE SUMMARY

In ―Dual Adaptive Watermarking for Biomedical Images‖ we mainly deal with the

watermarking procedures currently being carried out in the field of biomedical images,and also try to improve upon the same by proposing an improved scheme.

The digital form of medical images have a lot of advantages over its analog form such as

ease in storage and transmission. Medical images in digital form must be stored in a

secured environment to preserve patient privacy. It is also important to detect

modifications on the image. These objectives are obtained by watermarking in medical

images.

In this project we will mainly deal with the DWT(Discrete Wavelet Transform) for

watermarking of biomedical images. It is a method for decomposing an image into 4

subbands of varying frequencies, that aids in localization of values in an image, energy

compaction and also decorrelation of values, leading to greater security than normal

spatial domain based transforms.

We firstly employ a conventional DWT based watermarking scheme on a set of

biomedical images, and then apply attacks on them to ascertain their quality of

robustness and imperceptibility. We also employ a proposed scheme under which we

highlight the regions of interest and non-interest in an image, and apply separate

watermarks based on their desired requirements. This composite image, is tested by

applying several image processing and geometrical distortion attacks.

We will then compare the results of both, the original scheme and the proposed scheme

by comparing the state of the images after applying the attacks stated.



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TABLE OF CONTENTS

CERTIFICATE..……………………………………………………………………….………………..i ACKNOWLEDGEMENT…………………………………………………………………..................ii EXECUTIVE SUMMARY………………………………………………….………………………...iii 1. Introduction ………………………………………………………………………………………….1

1.1. Introduction…………………………………………………………………………………….1

1.2. Digital Images ………………………………………………………………………...……….1

1.2.1. Types of Images ………………………………………………………………………….1

1.2.2. Formats of images………………………………………………………………………2

1.3. Digital Image Watermarking ………………………………………………………….……….4

1.3.1. Introduction ……………………………………………………………………………....4

1.3.2. Structure of a Digital Watermark ……………………………….…………………….…5

1.3.3. Principle of Watermarking ……………………………………………………………….6

1.3.3.1. Watermark Insertion………………………………………………………… .....6

1.3.3.2. Watermark Generation………………………………………………………… ..6

1.3.3.3. Encoding Process………………………………………………………………. ..7

1.3.3.4. Watermark Extraction…………………………………………………………… 7

1.3.3.5. Decoding Process……………………………………………………………… ..7

1.3.3.6. Comparison Process………………………………………………………… ..….7

1.3.4. Requirements of Watermarking …………………………………………………… ...…..8 1.3.5. Applications of Watermarking …………………………………………………… ..…..9

1.3.6. Attacks o n Watermarks…………………………………………………………… ..….10

1.3.7. Types of Watermarking ………………………………………………………………...11

1.3.8. Watermark Embedding Techniques …………………………………………….... ........12

1.3.8.1. Spatial Domain Techniques ……………………………………………...... ......12

1.3.8.2. Transform Domain Techniques ……………………………………………… ...13

1.3.8.3. Contourlet Domain Techniques ………………………………………………13

2. Bio-Medical Image Watermarking ……………………………………………………………….14

2.1. Introduction …………………………………………………………………………...……….. 15

2.2. Security and Medical Information ………………………………………………………… .…15

2.3. Watermarking Medical Applications ……………………………………………………… .…17

2.4. Case Studies on Watermarking for Bio-medical Images ……………………………………18

2.4.1. Authentication and Tracing …………………………………………………………… .18



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2.4.2. EPR Diffusion …………………………………………………………………………..18

2.5. Requirements for Medical Image Watermarking ……………………………………………19

2.5.1. Reversible Watermarking ……………………………………………………………..19

2.5.2. Defining Regions of Interest and Regions of Insertion ………………………………...19

2.5.3. Integrity Control ………………………………………………………………………...19 2.5.4. Authentication …………………………………………………………………………..19

2.5.5. Dual Watermarking Scheme ……………………………………………………………20

2.5.6. Adaptive Watermarking ………………………………………………………………...20

3. Related Studies ……………………………………………………………………………………...21

3.1. General Watermarking System ………………………………………………………………..22

3.2. Watermarking in Spatial Domain ……………………………………………………………..22

3.2.1. Insertion of Watermark …………………………………………………………………22

3.2.2. Extraction of Watermark ………………………………………………………………..24

3.3. Watermarking in Transform Domain …………………………………………………………25

3.3.1. Discrete Cosine Transform (DCT) ……………………………………………………...25

3.3.2. Discrete Wavelet Transform (DWT) …………………………………………………...26

3.3.3. Singular Value Decomposition…………………………………………………………28

3.4 Watermarking in Contourlet Domain …………………………………………………………..31

3.5 Test Standards…………………………………………………………………………………..33

3.5.1 Peak Signal to Noise Ratio (PSNR)……………………………………………………..33 3.5.2 Structured Similarity Measure (SSIM)………………………………………….............33

3.5.3 Luminance Comparison (LC)………..…………………………………………………..33

3.5.4 Contrast Comparison (CC)………………………………………………………………34

3.5.5 Structured Comparison (SC)……………………………………………………………..34

3.5.6 Bit Error Rate (BER)…………………………………………………………….............34

3.5.7 Normalized Cross Correlation (NCC)…………………………………………………...34

4. Design and Implementa tion………….……………………………………………………….…….35

4.1 Implementation tools and pre-requisites …………………………………………… ..……..36

4.2 Algorithms Implemented………………….………………………………………………….37

4.2.1. Basic DWT Algorithm………..………………………………………………….……..37

4.2.1.1 Embedding……………………………………………………………………….….37

4.2.1.2 Extraction……………………………………………………………………………37



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4.2.2. DWT Dual and Adaptive Algorithm………….......…………………………….……… 38

4.2.2.1 Embedding in Region of Interest………………………………………………… ....38

4.2.2.2 Embedding in Region of Non Interest…………………………………………..….. 38

4.2.2.3 Extraction from Region of Interest……………………………………….... ............39

4.2. 2.4 Extraction from Region of Non Interest…………………………………………… 394.3. Attacks to test Robustness and Imperceptibility…………………………………… ..…… 40

4.3.1 Table showing comparison of PSNR values of original to proposed scheme………… .40

4.3.2 Table showing comparison of NCC values of original to proposed scheme…… .…… 41

5 Conclusion and Appendices……………………………………………………………………….. 45

5.1 Conclusion………………………………………………………………………………… ...…46

5.2 Appendices………………………………………………………………………………… ..….47

5.2.1 Ap pendix A : Attacks done on images………………………………………………… 47

5.2.2 Appendix B : Extracting Region of Interest and Region of Non- Interest………… .….. 48

REFERENCES ……………………………………………………………………………………….. 50

BIBLIOGRAPHY……………………………………………………………………………… .……. 52

PERSONAL DETAILS…………………………………………………………………………. ........53



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LIST OF FIGURES

Fig1.1 File formats

Fig.1.2 Watrmark Embedding

Fig.1.3 Watermark Extraction

Fig.1.4 Principle of watermarking

Fig.1.5 Watermark Embedder (Encoder)

Fig1.6 Simple Decoding Process

Fig1.7 Comparing Process

Fig.1.8 Primary requirements of Watermarking Algorithms

Fig 1.9 Watermark Attacks

Fig 1.10 Types of Watermarking

Fig 2.1 Example of Medical image tampering

Fig 2.2 Security Tools

Fig 3.1 Embedding and extraction of watermark

Fig 3.2 DCT Implementation Flowchart

Fig 3.3 DWT Implementation

Fig 3.4 DWT hybrid watermark embedding(using SVD)Fig 3.5 DWT watermark extraction block schematic

Fig 3.6 SVD based watermark Embedding Block Diagram

Fig 3.7 SVD based watermark Extracting Block Diagram

Fig3.8 Contourlet Domain embedding and extraction block schematicFig 4.1 Implementation of pure DWT algorithm (non-adaptive)

Fig 4.2 Region of interest and non-interest, before, and after watermarking procedure

Fig 5.1 Region of Interest and non-Interest



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CHAPTER 1

INTRODUCTION



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1.1. INTRODUCTION

In the recent years, medical images are produced from a wide variety of digital imaging equipments, such as computed

tomography (CT), magnetic resonance imaging (MRI), computed radiography (CR) and so forth. With the increasing use of

internet and appearance of new system such as picture archiving and communication systems (PACS), the usability of digital form of medical images has been increased . Images in digital imaging equipments can be printed on films or papers.

Moreover, in these equipments images with patient data in DICOM format can be stored on different types of storage media

such as CD or DVD. Insurance companies, hospitals and patients may want to change this data for various reasons.

Therefore, protecting medical images against this threat is necessary. Watermarking can be used as a solution.

1.2. DIGITAL IMAGES

A digital image is composed of a number of elements, each having a particular location and value. These elements are

referred as picture elements, image elements and pixels. Pixels is term used to denote elements of a digital image. A image

can be defined as a two- dimensional function f(x,y) where ‗x‘ and ‗y‘ are spatial coordinates and the amplitude of ‗f‘ at any

pair of coordinate s is called the intensity or the gray level of the image at that point. When ‗x‘, ‗y‘ a nd the amplitude values

of ‗f‘ are all finite and discrete, the image is known a Digital Image. A digital image can be represented naturally as a

matrix.

1.2.1 Types of Images

1. Bi-level Image : This is the black and white image having only two values. Each pixel in such type of image

requires only one bit for representation i.e. either 0 (for black) or 1(for white).

2. Gray-Scale Image : In Gray Scale Image, any pixel can have any of n values between 0 to n-1 where n is the

maximum number of bits required to represent any pixel value. There can be 2n shades of Gray.

3. Continuous- Tone Image : In continuous- tone images, the adjacent pixel values differ just by one or few units.

For eyes, it is very hard to distinguish the difference between the adjacent pixel values. Any pixel in such images

can be represented by either a single large image (Gray-scale) or by three components (in the case of a colour

image). Continuous tone images are generally natural images which are not having any sharp edges.

4. Discrete – Tone Image : These are generally graphical or synthetic images. These types of images have sharp

edges and no blurring effect. Artificial objects, lines, text have sharp and well-defined edges and therefore vary in

contrast from the rest of the background. These images are called artificial images.

5. Cartoon- like Image : These types of images consist of uniform areas, having uniform colors. The adjacent areas

have different colours.

6. DICOM Image : DICOM (Digital Imaging and Communications in Medicine )

is a standard for handling, storing, printing, and transmitting information in medical imaging. It includes a file

format definition and a network communications protocol. It is the international standard for medical images and

related information (ISO 12052). It defines the formats for medical images that can be exchanged with the data and

quality necessary for clinical use. DICOM is implemented in almost every radiology, cardiology imaging, and



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radiotherapy device (X-ray, CT, MRI, ultrasound, etc.), and increasingly in devices in other medical domains such

as ophthalmology and dentistry. With tens of thousands of imaging devices in use, DICOM is one of the most

widely deployed healthcare messaging standards in the world. There are literally billions of DICOM images

currently in use for clinical care. Since its first publication in 1993, DICOM has revolutionized the practice of

radiology, allowing the replacement of X-ray film with a fully digital workflow. Much as the Internet has become

the platform for new consumer information applications, DICOM has enabled advanced medical imagingapplications that have ―changed the face of clinical medicine‖. From the emergency department, to cardiac stress

testing, to breast cancer detection, DICOM is the standard that makes medical imaging work — for doctors and for

patients.

1.2.2 Formats of images

1.2.2.1 TIFF is a very flexible format that can be lossless or lossy. In practice, TIFF is used almost exclusively as

a lossless image storage format that uses no compression at all. Most graphics programs that use TIFF do not

compression. Consequently, file sizes are quite big. And TIF is the most versatile, except that web pages don'tshow TIF files.

1.2.2.2 PNG is also lossless storage format. However, in contrast with common TIFF usage, it looks for patterns

in the image that it can use to compress file size. The compression is exactly reversible, so the image is recovered

exactly. Feature of PNG is transparency for 24 bit RGB images. PNG is slightly slower to read or write.

1.2.2.3 GIF creates a table of up to 256 colors from a pool of 16 million. If the image has a fewer than 256

colors, GIF can render the image exactly. When the image contains many colors, software that creates the GIF uses

any of several algorithms to approximate the colors in the image with the limited palette of 256 colors available.Better algorithms search the image to find an optimum set of 256 colors. Sometimes the GIF uses the nearest color

to represent each pixel, and sometimes it uses ―error diffusion‖ to ad just the color of nearby pixels to correct for

the error in each pixel. Thus, GIF is ―lossless‖ only for images with 256 colors or less. For a rich, true color image,

GIF may ―lose‖ 99.998% of the colors. It is very good for web graphics.

1.2.2.4 JPEG is optimized for photographs and similar continuous tone images that contain many colors. It can

achieve astounding compression ratios even while maintaining very high image quality. JPG works by analyzing

images and discarding kinds of information that the eye is least likely to notice. It stores information as 24 bit

color.

1.2.2.5 RAW is an image output option available on some digital cameras. Though lossless, it is a factor of three

of four smaller than TIFF files of the same image. The disadvantage is that there is a different RAW format for

each manufacturer, and so you have to use the manufacturer‘s software to view thw images.



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1.2.2.6 BMP is an uncompressed proprietary format invented by Microsoft. There is really no reason to ever use

this fomat. The BMP file format (Windows bitmap) handles graphics files within the Microsoft Windows OS.

Typically, BMP files are uncompressed, hence they are large; the advantage is their simplicity and wide acceptance

in Windows programs.

Fig1.1 File formats

http://en.wikipedia.org/wiki/BMP_file_format






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1.3 DIGITAL IMAGE WATERMARKING

1.3.1 Introduction

Digital watermarking is the process of inserting a digital signal or pattern (indicative of the owner of the content) into digitalcontent. The signal, known as a watermark, can be used later to identify the owner of the work, to authenticate the content,

and to trace illegal copies of the work. The concept of digital watermarking is driven by the need to caption, and control

copyrights for digital media including images and video. Early work in the same identified redundant properties of an image

or its encoding that can be modified to encode watermarking information. The early emphasis was on hiding data, since the

envisioned applications were not concerned with signal distortions or intentional tampering that might remove a watermark.

However as watermarks are increasingly used for purposes of copyright control, robustness to common signal

transformations and resistance to tampering have become important considerations. Researchers have recently recognized

the importance of perceptual modeling and the need to embed a signal in perceptually significant regions of an image,

especially if the watermark is to survive lossy compression. However this requirement conflicts with the need for the

watermark to be imperceptible. There are several approaches that address these issues.

Recently there has been significant interest in watermarking. This is primarily motivated by a need to provide copyright

protection to digital content such as audio, images and video. Digital representations of copyrighted material such as movies

offer many advantages. However the fact that an unlimited number of perfect copies can be illegally produced is a serious

threat to the rights of content owners. Watermarking can be used for owner identification, to identify the content owner,

fingerprinting, to identify the buyer of the content, for broadcast monitoring to determine royalty payments, and

authentication, to determine whether the data has been altered in any manner from its original form. The latter purpose is

somewhat different from those of copyright control and the characteristics thereof may be different.

A number of technologies are being developed to provide protection from illegal copying. Two complimentary techniques

are encryption and watermarking. Encryption protects content during the transmission of the data. Watermarking

compliments encryption by embedding a signal directly into the data. Thus the goal of a watermark is to always remain

present in the data.

There are several properties that a watermark must exhibit. These include that it must be difficult to notice, robust to

common distortions of the signal, resistant to malicious attempts to remove the watermark, support a sufficient data rate

commensurate with the application, allow multiple watermarks to be added and that the decoder be scalable.



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1.3.2 Structure of a digital watermark

The structure of a digital watermark is shown in the following figures.

Fig.1.2 Watrmark Embedding

The material that contains a digital watermark is called a carrier. A digital watermark is not provided as a separate file or a

link. It is information that is directly embedded in the carrier file. Therefore, the digital watermark cannot be identified bysimply viewing the carrier image containing it. Special software is needed to embed and detect such digital watermarks.

Kowa‘s SteganoSign is one of these software packages. Both images and audio data can carry watermarks. A digital

watermark can be detected as shown in the following illustration.

Fig.1.3 Watermark Extraction

1.3.3 Principle of Watermarking

In general, any watermarking algorithm consists of three parts:

• The watermark (payload)

• The encoder (marking insertion/embedding algorithm )

• The decoder and comparator (verification or extraction or detection algorithm)



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Fig.1.4 Principle of watermarking

1.3.3.1 Watermark insertion:

Watermark insertion involves watermark generation and encoding process

Fig.1.5 Watermark Embedder (Encoder)

1.3.3.2 Watermark Generation:

Each owner has a unique watermark or an owner can also put different watermarks in different objects the marking

algorithm incorporates the watermark into the object. The verification algorithm authenticates the object determining boththe owner and the integrity of the object. The watermark can be a logo picture, sometimes a binary picture , sometimes a

ternary picture ; it can be a bit stream or also an encrypted bit stream etc. The encryption may be in the form of a hash

function or encryption using a secret key . The watermark generation process varies with the owner.



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1.3.3.3 Encoding Process:

In the encoding process both the original data and the payload data are passed through the encoding function. The payload

signal and the original host signal now together occupy space, which was previously occupied only by the host signal. For

this purpose either the original data is compressed or redundancy in digital content is explored to make space for the

payload.

1.3.3.4 Watermark Extraction:Extraction is achieved in two steps.

First the watermark or payload is extracted in the decoding process and then the authenticity is established in the comparing

process.

1.3.3.5 Decoding Process:

The decoding process can be itself performed in two different ways. In one process the presence of the original

unwatermarked data is required and other where blind decoding is possible. Fig.1.6 and Fig.1.7 show the two processes. A

decoder function takes the test data (the test data can be a watermarked or un-watermarked and possibly corrupted) whose

ownership is to be determined and recovers the payload.

Fig1.6 Simple Decoding Process

1.3.3.6 Comparison Process:

The extracted payload is compared with the original payload (i.e. the payload that was initially embedded) by a comparator function and a binary output decision is generated. The comparator is basically a correlator. Depending on the comparator

output it can be determined if the data is authentic or not. If the comparator output is greater than equal to a threshold then

the data is authentic else it is not authentic. fig.1.8 illustrates the comparing function. In this process the extracted payload

and the original payload are passed through a comparator. The comparator output C is the compared with a threshold and a

binary output decision generated. It is 1 if there is a match i.e. C >= δ and 0 otherwise. A watermark is detectable or

extractable to be useful . Depending on the way the watermark is inserted and depending on the nature of the watermarking

algorithm, the method used can involve very distinct approaches. In some watermarking schemes, a watermark can be

extracted in its exact form, a procedure we call watermark extraction . In other cases, we can detect only whether a

specific given watermarking signal is present in an image, a procedure we call watermark detection . It should be noted

that watermark extraction can prove ownership whereas watermark detection can only verify ownership .



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Fig.1.7 Comparing Process

1.3.4. Requirements of Watermarking

Digital watermarks can be measured on the basis of certain characteristics and properties that depend on the type of

vapplication. These characteristics and properties include the difficulties of notice, the survival of common distortions and

resistance to malicious attacks, the capacity of bit information, the coexistence with other watermarks, and the complexity

of the watermarking method.

In general, they are described as fidelity, robustness, fragility, tamper resistance, data payload, complexity, and other

restrictions. Digital watermarks must fulfil the following requirements.

Robustness

It may not be possible without knowledge of the watermark algorithm or secret key to remove the watermark or to make it

illegible. Robustness means the resistance ability of the watermark against the watermark attacks or modifications made to

the original file. After modifications, resizing, file compression, rotation, and common operations, the watermark can still

be detected and demonstrate a good quality.

Non-perceptibility

It means that the brought bit sample of the watermark does not produce perceptible changes acoustically or optically. A

perfect non-perceptible bit sample is present if data material marked with watermark and the original cannot be

distinguished from each other.

Non-detectable

It is always true that brought watermark information in data material is non-detectable if it is consistent with the origin data.

Undeletable

The watermarking must be hard to remove or even unable to remove by any attackers.

Complexity

Complexity describes the cost to detect and encode the watermark information. One of measurement technique could be the

amount of time. It is a good design to make watermarking algorithm and procedure as complex as possible.



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Capacity

Capacity refers to the amount of information that can be stored in a data source.

Unambiguous

The extracted watermark is equivalent to the embedded watermark.

A trade-off has to be taken between the above-mentioned criteria for an optimal watermarking application.

Fig 1.8. Primary requirements of Watermarking Algorithms

1.3.5 Applications of watermarking

1.3.5.1 Copyright Protection: This is by far the most prominent application of watermarks. With tons of images being

exchanged over insecure networks every day, copyright protection becomes a very important issue. Watermarking an

image will prevent redistribution of copyrighted images.

1.3.5.2 Authentication: Sometimes the ownership of the contents has to be verified. This can be done by embedding a

watermark and providing the owner with a private key which gives him an access to the message. ID cards, ATM cards,

credit cards are all examples of documents which require authentication.

1.3.5.3 Broadcast Monitoring: As the name suggests broadcast monitoring is used to verify the programs broadcasted

on TV or radio. It especially helps the advertising companies to see if their advertisements appeared for the right duration

or not.

1.3.5.4 Content Labeling: Watermarks can be used to give more information about the cover object. This process is

named content labeling.

1.3.5.5 Tamper Detection: Fragile watermarks can be used to detect tampering in an image. If the fragile watermark is

degraded in any way then we can say that the image or document in question has been tampered.

1.3.5.6 Digital Fingerprinting: This is a process used to detect the owner of the content. Every fingerprint will be

unique to the owner.



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1.3.5.7 Content protection: In this process the content stamped with a visible watermark that is very difficult to remove

so that it can be publicly and freely distributed .

1.3.6 Attacks on watermarks

Fig 1.9 Watermark attacks

A watermarked image is likely to be subjected to certain manipulations, some intentional such as compression and

transmission noise and some intentional such as cropping, filtering, etc. They are summarized below :

Lossy Compression : Many compression schemes like JPEG and MPEG can potentially degrade the data‘s quality

through irretrievable loss of data.

Geometric Distortions : Geometric distortions are specific to images and videos and include such operations as

rotation, translation, scaling and cropping.

Common Signal Processing Operations: They include the followings.

Resampling, Requantization, Dithering distortion, Recompression, Linear filtering such as high pass and low pass

filtering, Non-linear filtering such as median filtering, Color reduction, Addition of a constant offset to the pixel

values, Addition of Gaussian and Non Gaussian noise, Local exchange of pixels

Printing and Rescanning

Watermarking of watermarked image (rewatermarking)

Collusion : A Number of authorized recipients of the image should not be able to come together (collude) and like

the differently watermarked copies to generate an un-watermarked copy of the image (by averaging all the

watermarked images).

Forgery : A Number of authorized recipients of the image should not be able to collude to form a copy of

watermarked image with the valid embedded watermark of a person not in the group with an intention of framing a

3rd party.



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IBM attack : It should not be possible to produce a fake original that also performs as well as the original and also

results in the extraction of the watermark as claimed by the holder of the fake original.

1.3.7 Types of Watermarking

Fig 1.10 Types of watermarking

Invisible Watermarks

The watermark is embedded into the image in such a way that it cannot be perceived by human eye. It is used to protect the

image authentication and prevent it from being copied. Invisible watermark can be further divided into three types:

i. Robust Watermarks

Invisible watermark cannot be manipulated without disturbing the host signal. This is by far the most important requirement

of a watermark. There are various attacks, unintentional (cropping, compression, scaling) and unintentional attacks which

are aimed at destroying the watermark. So, the embedded watermark should be such that it is invariant to various such

attacks. They are designed to resist any manipulations that may be encountered. All applications where security is the main

issue use robust watermarks.

ii. Fragile Watermarks

They are designed with very low robustness. They are used to check the integrity of objects. A watermark is said to be

fragile if the information hidden within the host data is lost or irremediably altered as soon as any modification is applied to



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the host signal. Such a loss of information may be global, i.e. no part of watermarking can be recovered, or local i.e. only

part of the watermark is damaged. The main application of fragile watermarking is data authentication, where watermark

loss or alteration is taken as evidence that the data has been tampered with. The recovery of the information content within

the data demonstrates authentic un-tampered data. Robustness against signal distortion is better achieved if the watermark is

placed in perceptually significant parts of the signal. This is particularly evident in the case of lossy compression

algorithms, which operate by discarding perceptually insignificant data. Watermarks hidden within perceptuallyinsignificant data are likely not to survive compression. Achieving watermark robustness, and, to a major extent, watermark

security is one of the main challenges watermarking researches are facing with.

iii. Semi-fragile Watermarks

Watermark is semi-fragile if it survives a limited well specified, set of manipulations, leaving the quality of the host

document virtually intact. In some applications robustness is not a major requirement, mainly because the host signal is not

intended to undergo any manipulations, but a very limited number of minor modifications such as moderate lossy

compressions, or quality enhancement. This is the case of data labeling for improved actual retrieval, in which the hidden

data is only needed to retrieve the host data from archive, and thereby it can be discarded once the data has been correctly

assessed. Usually data is archived in compressed format, and that the watermark is embedded prior to compression. In this

case the watermark needs to be robust against lossy coding.

On the basis of method of extraction of watermark, watermarking algorithms can be classified as:

Non-Blind (Private)

Use the original signal/image to extract the embedded Watermark.

Semi-Blind (Semi Private)

Don‘t use the original signal, use side information and/or original watermark for extraction of watermark.

Blind (Public or oblivious)

Don‘t use original signal or side information for extraction of watermark.

1.3.8 Watermark embedding Techniques

a. Spatial Domain

b. Frequency Domain (Transform Domain)

c. Contourlet Domain

1.3.8.1 Spatial Domain techniques

These methods based on direct modification of the values of the image pixels, so the watermark has to be embedded in

this way. Such methods are simple and computationally efficient, because they modify the color, luminance or brightness

values of a digital image pixels, therefore their application is done very easily, and requires minimal computational

power. Spatial domain processes are expressed as

G ( x , y ) = T [ F ( x , y ) ]



13

G (x,y) : processed image

F (x,y) : input image

T : operator on F

1.3.8.2 Frequency Domain techniques

These methods are based on the using of some invertible transformations like discrete cosine transform (DCT), discrete

fourier transform (DFT), discrete wavelet transform (DWT) etc. to the host image. Embedding of watermar is made by

modifications of the transform coefiicients, accordingly to the watermark or its spectrum.

1.3.8.3 Contourlet Domain Techniques

In the contourlet transform (CT), the Laplacian pyramid (LP) decomposes an image into a low-frequency (LF) subband

and a high-frequency (HF) subband. The LF subband is created by filtering the original image with 2-D low-pass filter.

However, the HF subband is created by subtracting the synthesized LF subband from the original image but not by 2-D

high-pass filtering the original image. A contourlet-based image adaptive watermarking (CIAW) scheme, in which the

watermark is embedded into the contourlet coefficients of the largest detail subbands of the image. The transform

structure of the LP makes the embedded watermark spread out into all subbands likely in which the LF subbands are

included when we reconstruct the watermarked image based on the watermarked contourlet coefficients. Since both the

LF subbands and the HF subbands contain watermarking components, our watermarking scheme is expected to be robust

against both the LF image processing and the HF image processing attacks.



14

Chapter 2

Bio-Medical ImagesWatermarking



15

2.1 INTRODUCTION

Digital information management in hospitals, HIS (Hospital Information System), and its special cases of RIS (Radiology

Information System), PACS (Picture Archiving and Communication System) forms the information infrastructure of

modern health care. Recently the advent of multimedia has boosted the potential of telemedicine applications ranging from

teleconsulting, telediagnosis etc. to cooperative working session and telesurgery. These advances in information

and communication technology provide in fact new ways to store, access and distribute medical data, and introduces new

practices for the profession, as well as the patient themsleves by accessing to their own medical files. With these benefi ts

there are concomitant risks for electronic patient records (EPR) and strictly personal documents circulating in open

networks, and being accessible, e.g., via Internet. Thus it is a widely shared point of view that there is an urgent need for

network level security measures and protocols in medical information.

Fig 2.1 Example of Medical image tampering

It is becoming easier and easier to tamper with digital image in ways that are difficult to detect. For example Fig2.1 shows

two nearly identical images using readily available software (eg. Adobe Photoshop) the cyst was removed from the image

by using the healing brush tool. It is difficult if not possible to tell which picture is the original and which has been

tampered with. If this image were a critical piece of evidence in a legal case or police investigation, this form of tampering

might pose a serious problem.

2.2 SECURITY AND MEDICAL INFORMATION

Medical information record of a patient is a complex of clinical examinations, diagnosis annotations, prescriptions,

histological and other findings, and images in various modalities. In the digital format they are centered in the EPR

(Electronic Patient Record). This information is gathered over years by a number of health professionals and used as well

for different purposes (patient care but also clinical research, epidemiological studies or insurance companies). All patient

records, electronic or not, linked to the medical secrecy, must be confidential. The digital handling of EPR on network

requires a systematic content validation which is aimed at quality control: actuality (precise interest of the information at a

given instant) and reliability (authentication of the origin and integrity). Security of medical information, derived from strict



16

ethics and legislatives rules, gives rights to the patient and duties to the health professionals. This imposes three mandatory

characteristics: confidentiality , reliability , and availability .

Confidentiality means that only the entitled users, in the normally scheduled conditions, have access to the information.

Reliability has two aspects; i) Integrity : the information has not been modified by non-authorized people, and ii)

Authentication : a proof that the information indeed belongs to the correct patient and is issued from the correct source.

Availability is the ability of an information system to be used by the entitled users in the normal scheduled conditions of

access and exercise.

Fig 2.2 Security Tools

Security tools also have their limits . Regarding the information system access, firewalls provide a certain level of isolation

between the intra-net and Internet, but are easily bypassed by hackers. For storage and transmission, cryptography is

probably a very efficient tool, but once the sensitive data is decrypted, the information is not protected anymore.

Furthermore the file headers are in the plain-text format and can be usurped by a pirate. Cipher text, on the other hand,

unless protected by error correction facility, is very sensitive to bit errors occurring during storage and transmission. Once

the images are in the open (plain-text form), the major threat is the violation of the access rights and of the daily logs by the

intruder. Breaking of the confidentiality implies that integrity and authenticity cannot be guaranteed. Finally, a surprisingly

large proportion of authenticity problems are not due to any intrusion, but due to errors in the manual entry of patient data.

Watermarking is made to introduce identifiers which, by construction, are inseparable from the document they are attached

to. They may be seen as ultimate ramparts against usurpation and fabrication. It can be claimed that, in the medical domain,

watermarking is an additional tool in the repertoire of security measures, specifically adapted to images, which can be used

to thwart certain attacks.

The number of studies in the literature dedicated to watermarking is not very expensive. One of the earliest techniques

involved insertion of an encrypted version of the EPR in the LSB of the gray levels of medical-image levels . Although

the damage to the diagnostic image quality is minimal, the limitations and fragility of LSB watermarking can be

catastrophic. Another similar LSB technique was developed in which the image carrier authenticates the origin of the

Security

Confidentiality

Firewall EncryptionSoftware

AccreditationAccess Control

Reliability

Integrity

Firewall Access Control Antivirus DigitalSignature

Authentication

Availibility



17

transmission (hospital), and the message to be embedded is composed of an ECG record, the diagnosis report and the

doctor‘s seals. Attention is given to the trusted header by watermarking the root part in the image data.

2.3 WATERMARKING MEDICAL APPLICATIONS

To address the specific application needs of watermarking in the context of biomedical images, the three main objectives

foreseen are:

1. Data Hiding for the purpose of inserting metadata, annotations, and other information that just makes the image more

useful or easier to use.

2. Integrity Control , which is the verification that the image is intact, in that it has not been modified in an unauthorized

manner.

3. Authenticity , which is the verification that the image really is what the user supposes it is.

Within the medical domain there are two extremities that may be experimented and a large variety of intermediary cases

which may be more or less interpolated from these cases. At one extremity, one family of applications covers the

transmission of medical documents over public networks, like in telemedicine, remote or collaborative telediagnosis or

telesurgery, distance learning and several applications dealing with database consulting. In this case the demand is very

close from the desiderata of image watermarking as expressed for e-commerce or multimedia applications over open

networks. The images will face transmission errors and lossy compressions; the protocols will probably be heterogeneous;

secure modalities (firewalls, accredited software) will be rare and the end-terminals will not be secured. Under these

circumstances we find watermarking solutions very similar to the many developed in other domains of image

communication, the basic constraints of medical images being taken as additional guidelines for selecting the watermarking

method.

On the other extremity, within the hospital network and under the complete security system, very different problems will be

faced. Transmissions are done most of the time without loss and specific workstations with adequate protocols and software

may be available for handling local security problems. Under these conditions, security problems may only arise from either

malicious attempts to break the security protocol, or human negligence or mistakes.

The usual constraints of watermarking are:

1. Invisibility of the mark

2. Capacity (expressed in bit per host pixel)

3. Secrecy to unauthorized persons

4. Robustness to attempts to suppress the mark

These demands also exist in the medical domain but additional constraints are added.



18

2.4 CASE STUDIES ON WATERMARKING FOR BIOMEDICAL IMAGES

2.4.1 Authentication and Tracing

Medical images may go through several services and receive different processing and annotations. These transformations

are recorded in a historical resume that is attached to the image as metadata along with the patient references and the

acquisition data. If a watermark is introduced in the image carrying an identifier also present in the resume, it guarantees

that no error of falsification has been committed in the simultaneous processing of the image and the resume. The automatic

handling of the watermark and the resume by authorized workstations is an additional security for the documents and may

allow for instance the practice of reversible watermarking. If non reversible watermarking is used, incremental marks may

hold track of the different services that handled the document.

2.4.2 EPR Diffusion

If the EPR is kept by the patient, or distributed on several sites, and transmitted to the different services in charge of further

considerations or treatments, some mechanisms should be introduced to guarantee the integrity of the document. These

mechanisms not only will tell the medical persons whether the document is the same as the original one, but also if some

differences exist, their location and importance. The decision could then be taken to consider the document is valuable or

not.

As the checking of the watermark may be heavy, it is probably not likely that it is done every time the image is displayed.

The resume file attached to the image or the image header will be the prime identifier, and the control of the adequacy

between the image and the resume or header be made when necessary for instance before entering the documents in a

database or before a diagnosis or when a conflict happens. In this scenario the watermark is the ultimate security proof most

of the time ignored by the system, which is only used when important security issues arrive.



19

2.5 REQUIREMENTS FOR MEDICAL IMAGE WATERMARKING

2.5.1 Reversible Watermarking

Medical tradition is very strict with the quality of biomedical images, in that it is often not allowed to alter in any way the

bit field representing the image. Thus the watermark must be reversible, in that the original pixel values must be exactly

recovered. This limits significantly the capacity and the number of possible methods. It also constrains to have dedicated

routines to automatically suppress and introduce the mark in order to prevent the transmission of unprotected documents.

2.5.2 Defining Regions of Interest and Regions of Insertion

The watermark protects the regions of interest while being inserted in the rest of the image plane. One could be more

tolerant in the regions of non interest as they do not contribute to the diagnosis. For example to increase capacity and

robustness, one can allow the watermarking signal to be somewhat perceptible, provided its level does not disturb the

radiologist.

It has been shown that judicious alterations such as those occurring in image compression do not interfere with the

diagnosis ability. Therefore in time, the attitude demanding strict preservation of the images as a number field will be

relaxed. Thus watermark insertion methods that use the whole image, while bringing out imperceptible alterations in the

pixels will creep into the medical field as well.

2.5.3 Integrity Control

There is a need to prove that the images, on which the diagnoses or any insurance claims are based, have preserved their

integrity. One must define the ―start point‖ of integ rity, as the original captured image often must undergo certain

processing, like enhancement and contrast stretching, to be more useful to the radiologist. Thus it must be decided whichversion of the image, whether the pristine sensor output or the processed and standardized image at a certain stage by the

radiologist, is taken as the reference for integrity control. The integrity control based on the exact preservation of all the bit

planes of the image may be unnecessarily strict. Thus alternatives, specifically content-based integrity control are still open

to discussion.

2.5.4 Authentication

A critical requirement in patient records is to authenticate the different parts of the EPR, in particular the images. More

often the image is identified by an attached file or a header that carries all the needed information (e.g. the DICOM solution

to radiology images). However keeping the meta-data of the image in a separate header file is prone to forgeries or clumsy practices. An alternative would be to embed all such information into the image data itself. Another possible scheme is to

have both the DICOM header in a separate file and embed the digest of the same information in the image. An important

issue here is that how much information can be embedded. Medical data is more demanding in quality but less prone to

degradations, as compared to multimedia content. Hence tens of bits per Megabits of data is achievable within the medical

constraints.



20

Another variant application is MRI where the watermarking must satisfy the critical requirement that any arbitrary 2-D slice

extracted from this volume, even with the unknown slicing angle must provide sufficient authentication evidence on the

patient.

2.5.5 Dual Watermarking Scheme

We use a dual watermarking scheme to enhance confidentiality and authentication. We focus on two types of watermark hiding. In caption watermarking , by hiding patient‘s information in ROI, both authentication and confidentiality are

achieved and gives a permanent link between the patient and medical data. In signature watermarking , we hide the

patient‘s digital signature or identification code in RONI for the purpose of origin authentication.

2.5.6 Adaptive watermarking

To achieve better performance in terms of perceptually, invisibility and robustness, we use adaptive quantization parameters

for data hiding. Because the energy distribution is an important characteristic for digital image processing, we use a model

that employs this parameter for determining the adaptive quantization parameter. The embedding strength is more or less

proportional to the value of energy to have better robustness and transparency in this method.



21

Chapter 3

Related studies



22

3.1 GENERAL WATERMARKING SYSTEM

First, a host image (original image) directly embeds watermark in spatial domain or is transformed into frequency

domain through the well-known spread spectrum approach, i.e. DFT (Discrete Fourier Transform), DCT (Discrete Cosine

Transform) or DWT (Discrete Wavelet Transform). However, the algorithms using transform domain offer more robust

than directly embedding watermark into spatial domain. Then, coefficients are passed through a perceptual analysis block

that determines how strong of the watermark in embedding algorithm so that the resulting watermarked image is

imperceptible. The secret key uses to generate watermark and watermark embedding location more. The watermark is

embedded using a specific well-designed algorithm based on mathematical or statistical model. If the coefficients in

frequency domain, the inverse spread spectrum approach is then adopted to obtain a watermarked image.

The watermark extraction applies the similar operations in embedding processes. It employs the inverse operations or uses

the mathematical or statistical characteristic to extract the embedded watermark. Watermark detection decides whether an

image has been watermarked and the watermark exists or not.

Fig 3.1 Embedding and extraction of watermark

3.2 WATERMARKING IN SPATIAL DOMAIN

3.2.1 Insertion of Watermark

A block based spatial domain algorithm is used to hide copyright mark (invisible logo) in the homogenous regions of the

cover image exploiting average brightness.

3.2.1.1 The cover image is partitioned into non-overlapping squares of size (8X8) pixels. A block is denoted by the location

of its starting pixel (x,y). If the cover image is of size (NXN), total (N/8,N/8) number of such blocks are obtained for

watermark insertion. Next, all such blocks are arranged in ascending order based on their variance values. The variance of a

block of size (mXn) is denoted by:



23

Is the statistical average value of the block.

The blocks having small variance values may be called as homogenous blocks and, of course, the smallness in variance

values depends on the characteristics of image to be watermarked. If the watermark symbol is a (NXN) binary image, only

N2 homogenous blocks are sufficient to insert one watermark pixel in each homogenous block. A two level map of size

(N/8 X N/8) is constructed based on the location of homogenous blocks in the cover image assigning each homogeneous

block of the cover image by value ‘1‘ while all other blocks by value ‘0‘.Thistwo level map later modified as multi level

image, also called as secret image (s), is used for extraction of watermark pixels.

3.2.1.2 One watermark pixel is inserted in each homogenous block. Before insertion, the binary watermark is spatially

dispersed using a chaotic system called‖torus automorphism‖. Basically, the torus automorphism is a kind of image

independent permutation done by using pseudo random number of suitable length. This pseudorandom number is generated

using ‖Linear Feedback Shift Register‖. The pseudo random number in the present case is of length 256 and the spatially

dispersed watermark data thus obtained is denoted by L1.

3.2.1.3 From the two level image formed in step 2, desired blocks of the cover image are selected and statistical average

value of these blocks are used for watermark insertion. Let for one such block this average value and its integer part are

denoted by A and Ai respectively. Now one pixel from L1 replaces a particular bit (preferably Least Significant Bit planes)

in bit plane representation of A for each homogenous block. The selection of particular bit in bit plane representation may

be determined based on the characteristics (busyness /smoothness of regions) of the block. The bit plane selection is also

governed by global characteristics of the cover image besides the local property of candidate block, such as mean gray

value. For a block of low variance (homogenous zone) higher bit plane may be chosen provided that the mean gray level

value of the block is either less than T2 or greater than T1 where T1 and T2 are certain pre-specified threshold values which

should preferably be close to ‘0‘ (minimum) and close to ‘255‘ (maximum).

However, the ‘closeness‘ of T1 and T2 to ‘0‘ and‘255‘ respectively, is relative, and is strongly image dependent. Users may

choose the value of T1 and T2 and also the proper bit plane by checking the degradation in the image quality affected by the

insertion of the logo. A multilevel secret image is constructed by inserting the value of bit position selected for different

homogenous block located in the ‘1‘ position of the secret image. This positional information as gray value of the secret

image helps to extract watermark pixel from the proper bit position of the mean gray value of the block.

3.2.1.4: The choice of lower order MSB plane (say 3rd or higher from the bottom plane) may result in more robust

watermarking at the cost of greater visual distortion of the cover image. Further bit manipulation is done to minimize this



24

aberration and to counter the effect of smoothing that may cause possible loss of embedded information. The process

effectively changes those mean gray values of the blocks that have been used in watermark insertion. Implementation is

done by estimating the tendency of possible change in mean gray value after the attack like mean filtering. Larger size of

spatial mask such as (7X7) is used to adjust suitably the gray values of all pixels of the block. The use of spatial mask

reduces visual distortion on and average fifty percent times.

3.2.2 Extraction of Watermark

The extraction of watermark requires the secret image(s) and the key (k) used for spatial dispersion of the watermark image.

The watermarked image under inspection with or without external attacks is partitioned into non-overlapping block of size

8x8 pixels. Now from the secret image, position of the homogenous blocks are selected and gray value of the secret image

indicates the corresponding bit position in mean gray values where watermark pixel was inserted. Hence from the secret

image the mean gray value of the blocks of the watermarked image/distorted watermarked image is calculated and

watermark pixel is extracted. The spatially dispersed watermark image thus obtained is once again permuted using the same

key (k) (pseudo random number) and watermark in original form is thus obtained. This completes watermark extraction

process .A quantitative estimation for the quality of extracted watermark image with reference to the original watermark

may be expressed as normalized cross correlation

Maximum value of NCC is unity.



25

3.3 WATERMARKING IN TRANSFORM DOMAIN

3.3.1 Discrete Cosine Transform (DCT)

The DCT makes a spectral analysis of the signal and orders the spectral regions from high to low energy. It can be applied

globally or in blocks. When applied globally, the transform is applied to all parts of the image, separating the spectral

regions according to their energy. When applied in blocks, the process is analogous, only the transform is applied to each

block separately.

The typical algorithm steps are:

1) Segment the image into non-overlapping blocks of 8x8;

2) Apply forward DCT to each of these blocks;

3) Apply some block selection criteria;

4) Apply coefficient selection criteria;

5) Embed watermark by modifying the selected coefficients

6) Apply inverse DCT transform on each block.The formulae for DCT transform and inverse DCT transform are given as follows:

The human eyes are more sensitive to noise in lower-frequency band than higher frequency. The energy of natural image isconcentrated in the lower frequency range. The watermark hidden in the higher frequency band might be discarded after a

lossy compression. Therefore, the watermark is always embedded in the lower-band range of the host image that

transformed by DCT is perfect selection.



26

Fig 3.2 DCT Implementation Flowchart

3.3.2 Discrete Wavelet Transform (DWT)

The DWT can be implemented as a multistage transformation. An image is decomposed into four sub bands denoted LL,

LH, HL, and HH at level 1 in the DWT domain, where LH, HL, and HH represent the finest scale wavelet coefficients and

LL stands for the coarse-level coefficients. The LL sub band can further be decomposed to obtain another level of

decomposition. The decomposition process continues on the LL sub band until the desired number of levels determined by

the application is reached. Since human eyes are much more sensitive to the low frequency part (the LL sub band), the

watermark can be embedded in the other three sub bands to maintain better image quality.

Fig 3.3 DWT Implementation



27

WATERMARK EMBEDDING ALGORITHM

Step 1 . For a given Ν×Ν image, apply the discrete wavelet Transform up to 3 rd level, which produce a total of 9 bands

wavelet coefficients.

Step 2. The middle frequency sub-bands HL and LH from 2 nd level of wavelet decomposition are used to get 3 rd level

decomposition.

Step 3. The watermark of size M×M is converted into binary pattern.

Step 4. The binary image is scaled to the size of original host image and then duplicated.

Step 5. Pseudo random sequence is generated using a secret key and combined with the duplicated watermark to increase

robustness.

Step 6. The resultant watermark is then embedded into the middle frequency sub-bands of host image.

Step 7. Finally, inverse DWT is performed to produce the watermarked image.

Fig 3.4 DWT hybrid watermark embedding(using SVD)

WATERMARK EXTRACTION ALGORITHM

Step 1. For a given Ν×Ν watermarked image, apply the discrete wavelet Transform up to 3 rd level, which produce a total of

9 bands of wavelet coefficients.

Step 2. The middle frequency sub-bands HL and LH from 2 nd level of wavelet decomposition are used to get 3 rd level

decomposition.

Step 3. Same secret key used in the embedding process enables to generate the random sequence.

Step 4. Finally, the watermark is extracted from the selected wavelet coefficients.

Step 5. After extracting the final watermark, compares it with the original watermark, to find the any attacks happened in

the original data.



28

Fig 3.5 DWT watermark extraction block schematic

The DWT technique provides better imperceptibility and higher robustness against attacks, at the cost of the DWT

compared to DCT schemes. Each watermark bit is embedded in various frequency bands and the information of the

watermark bit is spread throughout large spatial regions. As a result, the watermarking technique is robust to attacks in both

frequency and time domains. . However, improvements in its performance can still be obtained by viewing the image

watermarking problem as an optimization problem.

The DWT technique provides better imperceptibility and higher robustness against attacks, at the cost of the DWT

compared to DCT schemes. Each watermark bit is embedded in various frequency bands and the information of the

watermark bit is spread throughout large spatial regions. As a result, the watermarking technique is robust to attacks in both

frequency and time domains. . However, improvements in its performance can still be obtained by viewing the image

watermarking problem as an optimization problem.

3.3.3 SINGULAR VALUE DECOMPOSITION (SVD)

SVD is one of a number of effective numerical analysis tools used to analyze matrices. In SVD transformation, a matrix can be decomposed into three matrices that are the same size as the original matrix. Given a real n · n matrix A, this matrix can

be transformed into three components, U, D and V, respectively, such that

where the U and V components are n x n real unitary matrices with small singular values, and the D component is an n x n

diagonal matrix with larger singular value entries.

A‘ is the reconstructed matrix after the inverse SVD transformation.



29

Using SVD in digital image processing has some advantages:

i. The size of the matrices from SVD transformation is not fixed and can be a square or a rectangle.

ii. Singular values in a digital image are less affected if general image processing is performed.

iii. Singular values contain intrinsic algebraic image properties.

iv.

SVD Based Watermarking

In 2002, Sun et al. proposed an SVD and quantization-based watermarking scheme (Sun et al., 2002). The D component

with a diagonal matrix was explored.

In the embedding procedure, the largest coefficients in D component were modified and used to embed a watermark. The

modification was determined by the quantization mechanism. After that, the inverse of the SVD transformation was

performed to reconstruct the watermarked image. Because the largest coefficients in the D component can resist general

image processing, the embedded watermark was not greatly affected. Also, the quality of the watermarked image can be

determined by the quantization. Thus, the quality of the watermarked image quality can be maintained.

To extract an embedded watermark, the SVD transformation was employed and the largest coefficients in the D component

were examined. After that, the watermark was extracted.

The watermark embedding and extracting procedures can be described as follows:

The watermark embedding procedure:

Step 1 . Partition the host image into blocks.

Step 2 . Perform SVD transformation.

Step 3. Extract the largest coefficient D(1, 1) from each D component and

quantize it by using a predefined quantization coefficient Q.

Let Z = D(1, 1)modQ.

Step 4. For an embedded watermark bit valued of 0, if Z < 3Q/4, D(1, 1) modify

to D‘(1, 1) = D(1, 1) + Q/4 - Z. Otherwise, D‘(1, 1) = D(1, 1) + 5Q/4 - Z.

Step 5. For an embedded watermark bit valued of 1, if Z < Q/4, D(1, 1) modify to

D‘(1, 1) = D(1, 1) - Q/4 + Z. Otherwise, D‘(1, 1) = D(1, 1) + 3Q/4 - Z.

Step 6. Perform the inverse of the SVD transformation to reconstruct the Watermarked image.



30

Fig 3.6 SVD based watermark Embedding Block Diagram

The watermark extracting procedure:

Step 1 . Partition the watermarked image into blocks.

Step 2. Perform SVD transformation.

Step 3. Extract the largest coefficient D‘(1, 1) from each D component and quantize it by

using the predefined quantization coefficient Q. Let

Z = D‘(1, 1)modQ.

Step 4. If Z < Q/2, the extracted watermark has a bit value of 0. Otherwise, the

extracted watermark has a bit value of 1.

Fig 3.7 SVD based watermark Extracting Block Diagram

Watermarked image

Inverse SVD Transformation

Modify D component according to value of watermark bit value

Quantize D component

Detect D component

SVD Transformation

Block Partition

Host image

Extracted watermark

Examine D component

Quantize D component

Detect D component

SVD Transformation

Block Partition

Watermarked image



31

3.4 WATERMARKING IN CONTOURLET DOMAIN

The contourlet transform, is a relatively new image decomposition scheme, which provides a flexible multiresolution

representation for 2D signals. It makes use of the Laplacian pyramid decomposition (LPD) for the multiresolution

representation of the image. In the contourlet transform, the Laplacian pyramid decomposes an image into a low frequency

subband into a high frequency subband. After this, a directional decomposition is performed on every band-pass image

using directional filter banks (DFB). The contourlet transform is unequaled since the number of directional bands could be

indicated by the user at any resolution. Finally, the image is represented as a set of directional subbands at multiple scales.

Discrete contourlet transform is able to capture the directional edges and contours superior to DWT. Even though other

transform domains only conform to grayscale images, this domain can conform to DICOM images as well, which is our

area of work.

Before the embedding process the following preprocessing steps are carried out:

1. For each row of the image, the left and right edges of the image are recorded, similarly for each column of the image,

the top and bottom edges of the image are recorded too. For an image of dimensions MxN, the left and right edges of the image form two vectors L and R of size M, and the upper and lower edge of the image construct two vectors T and

B of size N. For each vector we select l = min(L), r = max(R), t = min(T) and b = max(B), and then we define a

rectangle of which the left corner has coordinates (t,l) and the bottom right one is (b,r).

2. Watermark is reshaped to binary vector (W={w 1,w2,w 3,….,w k },w k E [0,1]).

3. In view of the robustness, we choose I L, lowpass subband of decomposed IO, for embedding and W is embedded into I L

in contourlet domain. For more invisibility the embed process can be done in the detail subbands.

EMBEDDING PROCESS

IL is divided into non-overlapping blocks A i of size b x b, i=1,2,….,M, where M is the number of the blocks. The energy

value of each block Ai is computer according to:

For each block Ai, the adaptive quantization step value δi is computed as follows.

Where δo is the basic quantization step tha t is different in ROI and RONI and served as a secret key, and the function floor

represents the round off operation. Using singular value decomposition (SVD), the singular value vectors of each block A

are computed.

By the singular values of each block Ai, N si= ||S|| + 1 is computed (where ||·|| represents the Euclidean norm) and quantized

by adaptive quantization step di that represents the quantization level as follows:



32

Finally using the value , the modified singular values are computed as follows:

Using these modified singular values, the watermarked block is obtained.

EXTRACTION PROCESS

For watermark extraction, we require only the size of the binary vector (W), and basic quantization step (δo). The

watermarked image is converted into the contourlet domain and the lowpass subband I L ‗is selected for extraction. At first I L

‗ is divided into non -overlapped blocks Ai of size b X b,i=1,2,…M where M is the size of the blocks. Then

is computed and quantized by adaptive quantization step that is computed similar to embedding process.



33

Fig3.8 Contourlet Domain embedding and extraction block schematic

3.5 TEST STANDARDS

3.5.1 Peak Signal to Noise Ratio (PSNR)

Peak Signal to Noise Ratio (PSNR) is used frequently as an objective image quality metric, but it does not consider

characteristics of the human visual system (HVS). It is poor at comparing different watermarking methods, but provides a

simple indicator for quantifying the similarity between original and watermarked images. PSNR uses peak power of the

original image and the mean squared value of the error signal.

PSNR is expressed as follows:

3.5.2 Structured Similarity Measure (SSIM)

The second measure used in this paper is structural similarity measure (SSIM) index, which is a region-based numerical

metric that places more emphasis on the HVS than PSNR. This metric is ideal for testing the similarities in medical images

because It focuses on local rather than global image similarity. Mathematically, for regions, it is expressed as:

SSIM compares the similarity in luminance (LC), contrast (CC), and structure (SC) of image regions for each pair of

corresponding blocks. a, b,and l are = 1and are used to weight the importance of each of the three components .

3.5.3 Luminance Comparison (LC)

Luminance comparison is a function of corresponding blocks‘ mean intensity and is given by



34

Where μ Io and μIo‘ are the mean values of regions respectively and c1 is a constant.

3.5.4 Contrast Comparison

Contrast comparison is a function of corresponding blocks‘ standard deviation and is expressed as

3.5.5 Structured Comparison

The structured comparison is given as the correlation coefficient and is given by:

Where C IoIo‘ is the correlation coefficient between the two regions and c3 is constant.

3.5.6 Bit Error Rate (BER)

We use BER to calculate the difference between the recovered EPR data and the original EPR data.

where wi and wi‘ are the original and recovered EPR vectors respectively. In the lack of adverse attacks, BER is fo und to be

zero.

3.5.7 Normalized Cross Correlation

It is also calculated to quantitatively analyze the likeness of the extracted watermark and the original watermark (logo) in

signature watermark. Where V(i,j) and V‘(i,j) are the original and extracted logos and M1, M2 are the size of the logoimage



35

Chapter 4Design and Implementation



36

4.1 IMPLEMENTATION TOOLS AND PRE-REQUISITES

We are using MATLAB version 2008b as our platform for working on our project. The image processing toolbox provided

inbuilt into the software provides a wide variety of tools to exploit.

We can perform image enhancement, image deblurring, feature detection, noise reduction, image segmentation, geometric

transformations, and image registration. Many toolbox functions are multithreaded to take advantage of multicore and

multiprocessor computers.

Image Processing Toolbox supports a diverse set of image types, including high dynamic range, gigapixel resolution,

embedded ICC profile, and tomographic. Graphical tools let us explore an image, examine a region of pixels, adjust the

contrast, create contours or histograms, and manipulate regions of interest (ROIs). With toolbox algorithms we can restore

degraded images, detect and measure features, analyze shapes and textures, and adjust color balance.

Besides the toolbox, we also required a rich repository of DICOM images and other grayscale biomedical images to work

with. The same is maintained at personal systems for convenience (over 50 images).

Test Images:

―11.tif‖ ―1.tif‖ ―9.tif‖

―juet_logo.tif‖



37

4.2 ALGORITHMS IMPLEMENTED

4.2.1 BASIC DWT ALGORITHM

4.2.1.1 EMBEDDING

1. Read Cover Image. 2. Read Watermark Logo. 3. Apply 1-level DWT to cover image and watermark obtained in step 1 & 2 to obtain LL,LH,HL and HH subbands.

4. Take alpha value (non adaptive method)

5. Applyling the alpha value for each subband separately

6. Apply inverse DWT to obtain final watermarked image.

7. Check for imperceptibility of the final watermarked image obtained above against the original image by evaluating

the PSNR values between them for various values of embedding strength (alpha)

4.2.1.2 EXTRACTION

1. Apply DWT to watermarked image to obtain extraction subbands

2. Obtain subband of watermark by using the extraction subband obtained above and the value of alpha ascertained

previously.

3. Apply IDWT on above components to obtain extracted watermark.

4. Check for consistency of extracted watermark by applying evaluation parameters (PSNR,NCC) against it.

Fig 4.1 Implementation of pure DWT algorithm (non-adaptive)



38

ALPHA PSNR

0.1 105.9469

0.2 92.4836

0.3 86.7155

0.4 83.7426

0.5 81.74260.6 80.8158

Table 4.1 Variation of PSNR with embedding strength(non adaptive)

4.2.2 DWT DUAL AND ADAPTIVE ALGORITHM

4.2.2.1 EMBEDDING IN REGION OF INTEREST (ROI)

1. Read the cover image and watermark logo.

2. Select n points from the current axes and return x and y coordinates in the column vectors x and y.

3. Obtain the binary mask BW for the ROI by setting the pixels inside the region of interest to 1 and those outside the

region to 0.

4. Extract the region of interest from the original image using the mask obtained above, by copying intensities into

the region specified by the user.

5. Apply DWT on the original and the watermark image to obtain subbands for both.

6. Obtain the embedding strength(alpha) using the mean from all values in the ROI.

7. Apply the watermark to every subband separately including LH,HL and HH subband.

8. Apply inverse DWT to obtain the watermarked image.

4.2.2.2 EMBEDDING IN REGION OF NON INTEREST (RONI)

1. Read the watermark image.

2. Apply 1-level DWT to both cover and watermark images to obtain subbands for both.

3. Obtain the embedding strength using the standard deviation of all values in the RONI

4. Apply the watermark to every subband separately in LL,LH,HL and HH subband.

5. Apply 1-level inverse DWT to obtain the watermarked image for RONI region.

6. To obtain the final watermarked image, containing watermarks of different quality in ROI and RONI, we replace

the intensity values of this said image with intensities of watermarked images of ROI and RONI at their respective

positions.

7. We check the robustness and imperceptibility qualities of the images obtained using NCC and PSNR parameters.



39

4.2.2.3 EXTRACTION FROM ROI

1. Using the matrix obtained from algorithm 4.2.2.1, extract the subbands of the image using 1-level DWT

2. Using the value of alpha obtained in algorithm 4.2.2.1, obtain the subbands of the watermark image embedded in

ROI region.

3. Obtain extracted ROI watermark by applying 1-level inverse DWT to the subbands obtained above.

4.2.2.4

EXTRACTION FROM RONI1. Using the matrix obtained from algorithm 4.2.2.2 extract the subbands of the image using 1-level DWT

2. Using the value of alpha obtained in algorithm 4.2.2.2 obtain the subbands of the watermarked image embedded

in RONI region.

3. Obtain extracted RONI watermark by applying 1-level inverse DWT to the subbands obtained above.

Fig 4.2 Region of interest and non-interest, before, and after watermarking procedure



40

4.3 ATTACKS TO TEST ROBUSTNESS AND IMPERCEPTIBILITY QUALITIES

A total of five attacks (and one compression test) have been used to determine the robustness and imperceptibility qualities

of our proposed scheme. After applying each attack, the corresponding values of NCC and PSNR are recorded and

compared with the original DWT scheme, to check for improvements / degradations.

4.3.1 TABLE SHOWING COMPARISION OF PSNR VALUES OF ORIGINAL AND PROPOSED SCHEME

We obtain the adaptive values of alpha from algorithms 4.2.1.1 and 4.2.1.2, and use the value of alpha obtained from RONI

to obtain PSNR values for the original scheme. This way, we have a precise comparison of values where the images used

and embedding strength employed is same in both the schemes.

ATTACK ALPHA VALUES PSNR (ORIGINAL) PSNR(PROPOSED)

Salt and Pepper 0.0953

92.7187 96.47380.3381

Motion Blurring

0.0964

92.7127 96.03440.3411

Standard Blur 0.0616

92.4576 95.05130.3356

Gaussian Blur 0.0473

92.5213 94.81080.3527

Sharpening0.0448

92.4837 94.78780.3540

Compression (75%)0.1018

59.5353 96.5642

0.3399


59.1245 94.85070.3518


58.7738 94.19590.3477


58.6858 93.89470.3534



41

4.3.2 TABLE SHOWING COMPARISON OF NCC VALUES OF ORIGINAL AND PROPOSED SCHEME

We applied each attack separately, and then recorded NCC values for both the extracted ROI watermark as well as the

RONI watermark. We have applied each attack on the watermarked image, and then extracted the watermarks in ROI and

RONI from this attacked image. This way, we can evaluate the effect of each attack on the extracted watermark, as

compared to the original scheme.

ATTACK ALPHA

VALUES

(ROI AND

RONI)

NCC

(ORIGINAL)

FIGURES

(ROI AND RONI EXTRACTED

WATERMARKS)

NCC

(PROPOSED)

Salt and Pepper

0.0953

0.5927

0.7274

0.3381 0.9128

Motion Blurring

0.0964

0.6153

0.9128

0.3411 0.9512



42

Standard Blurring

0.0616

0.6174

0.9113

0.3356 0.9445

Gaussian Blur

0.0473

0.9442

0.9631

0.3527 0.9398

Sharpening

0.0448

0.4170

0.8707

0.3540 0.8956



43

Compression

(75%)

0.1018

1.000

0.6878

0.3399 0.4112

Compression

(50%)

0.0578

1.000

0.8106

0.3518 0.4205

Compression

(25%)

0.0746

1.000

0.7693

0.3477 0.4837



44

Compression

(15%)

0.0482

1.000

0.8381

0.3534 0.4277



45

Chapter 5

Conclusion



46

5.1 CONCLUSION

―Dual Adaptive Watermarking for Bio -medical images‖ is a new watermarking method for DICOM images (various types)

based on using different embedding strength for ROI and RONI in order to not affect the interpretation by medical

specialists. The algorithm will use an automatically selection for ROI and embed the watermark in the singular values of

contourlet subbands that makes the algorithm more efficient, and robust against noise attacks than other transform domains.

The watermarked image can still conform to the DICOM format.



47

5.2 APPENDICES

5.2.1 APPENDIX A : ATTACKS DONE ON IMAGES

A1: COMPRESSION ATTACK

The most used image compression is definitely JPEG. In MATLAB, for compressing an image to different quality factors,the image should be created from a matrix and be reread:

imwrite(wc_image,'extracted_DWT.jpg','Mode','lossy','Quality',75);A = imread (‘extracted_DWT.jpg’);

A2: SALT AND PEPPER ATTACK

We can add a variety of noises into an image using the imnoise command in MATLAB.

A = imread('Watermarked_DWT.tif');A = imnoise(A,'salt & pepper',0.02);imshow(A);

A3: MOTION BLURRING

Motion blurring can be achieved through the following code that is inbuilt into MATLAB:

h = fspecial('motion', len, theta)

It returns a filter to approximate, once convolved with an image, the linear motion of a camera by len pixels, with an angle

of theta degrees in a counterclockwise direction. The default value of len is 9 and theta is 0, meaning a horizontal motion of

9 pixels.

H = fspecial('motion',20,45);

MotionBlur = imfilter(wc_image,H,'replicate');

imwrite(MotionBlur,’MotionBlur_DWT.tif’,’tif’);

imshow(MotionBlur)

A4: STANDARD BLURRING

h = fspecial('disk', radius) It returns a circular averaging filter (pillbox) within the square matrix of side 2*radius+1. The default radius is 5.H = fspecial('disk',10);

blurred = imfilter(wc_image,H,'replicate');imwrite(blurred,’Blurred_DWT.tif’,’tif’); imshow(blurred)

A5: GAUSSIAN BLURRING

h = fspecial('gaussian', hsize, sigma)

It returns a rotationally symmetric Gaussian lowpass filter of size hsize with standard deviation sigma (positive). hsize can

be a vector specifying the number of rows and columns in h, or i t can be a scalar, in which case h is a square matrix.



48

A6: STANDARD SHARPENING

h = fspecial('unsharp', alpha)

It returns a 3-by-3 unsharp contrast enhancement filter. alpha controls the shape of the Laplacian and must be in the range

0.0 to 1.0. The default value for alpha is 0.2.

Code:

H = fspecial('unsharp',0.5);sharpened = imfilter(wc_image,H,'replicate');imwrite(sharpened,’sharpened_DWT.tif’,’tif’); imshow(sharpened)

5.2.2 APPENDIX B : EXTRACTING REGION OF INTEREST AND NON-INTEREST

The following MATLAB code demonstrates how to select a region of interest from an image.

clc; clear all ; close all ; a=im2double(imread( '11.tif' )); imshow(a); [r,c]=ginput(4); BW=roipoly(a,r,c); [R C]=size(BW); %......Extracting ROI....................% for i= 1 : R

for j = 1 : C if BW(i,j)==1

out1(i,j)=a(i,j); else

out1(i,j)=0; end end

end %............Extracting RONI..............% for i= 1 : R

for j = 1 : C if BW(i,j)==1

out2(i,j)=0; else

out2(i,j)=a(i,j); end

end end %......Printing original image, ROI and RONI......% subplot(1,3,1), image(a), title( 'Original image' ); subplot(1,3,2), image(im2uint8(out1)), title( 'ROI' ); subplot(1,3,3), image(im2uint8(out2)), title( 'RONI' );



49

Fig 5.1 Region of Interest and non-Interest



50

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PERSONAL DETAILS

NAME ENROLMENT NUMBER PHOTOGRAPH

MANJARI TYAGI 091237

PALLAVI JAIN 091310

TAPAS TRIVEDI 091324

Dual Adaptive Watermarking for Biomedical Images

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