Wavelet Watermarking on Medical Images

Proceeding of the International Conference on Artificial Intelligence in Computer Science and ICT(AICS 2013), 25 -26 November 2013, Langkawi, MALAYSIA. (e-ISBN 978-967-11768-3-2). Organized by WorldConferences.net 147

WAVELET WATERMARKING ON MEDICAL IMAGES

Mohammad Abdullatif1, Akram M. Zeki

2, Jalel Chebil

3

International Islamic University Malaysia, Malaysia [email protected]

[email protected] [email protected]

Abstract

Digital image watermarking have been developed widely in recent years. One of the most important

applications of digital image watermarking is medical applications. Image watermarking can be used in

medical images for several purposes. It’s used to protect the patient’s information from unauthorized

people. In addition, it can be used for authentication if the patient lost his/her image. Moreover, it is

needed to protect the copyright of the medical images. This paper focuses on using wavelet transform in

medical images watermaking. It discusses the wavelet transform watermarking technique. And it

highlights the latest related work done on using wavelet transform watermarking over medical images.

Keywords: Watermarking, Wavelet, Medical Images

1. Introduction

The Internet has become very popular tool to transfer files in recent years. Different types of data such

as texts, sounds, images, and videos are being transmitted through Internet. Therefore, many issues

have been facing researchers related to confidentiality, reliability, and availability, and many other

issues. As a proper solution, digital image watermarking is used to solve these problems and other

problems. The basic idea of digital image watermarking stands on inserting information (watermark)

into a host image, then this watermarked image will be transmitted, and it can be extracted at the

receiver to get the information back. Medical Images such as CT and MRI are being transmitted

through Internet nowadays, thus, to protect patients’ privacy; digital image watermarking are being

used (Jingbing, Yong, Wencai, & Yen-wei, 2011b).

By embedding the watermarks into medical images, many goals can be achieved. First, it can protect

the patients’ information from unauthorized people. Second, the watermarked image authenticates the

patient if the patient lost his/her medical image. Third, it’s used to protect the copyright and integrity for

the medical image (Zeki, Manaf, Foozy, & Mahmod, 2011). In medical images, patient’s information,

doctor diagnosis and Electronic Patient Records (EPR) can be embedded in the image as hidden

watermarks (Jingbing, Xianhua, Chunhua, & Yen-wei, 2011). In addition doctor’s identification code

(DIC) can be embedded in the medical image (Memon, Gilani, & Qayoom, 2009).

This paper is organized as following: Section 3 describes wavelet watermarking technique. This is

followed by Section 4 that highlights the latest related work done on using wavelet transform

watermarking over medical images. Section 5 discusses multiple watermarks schemes. Next, section 6

presents integer wavelet transform. Finally, Section 7 highlights the algorithms that combine wavelet

with other techniques.

mailto:[email protected]

mailto:[email protected]


2. Basic Model of Digital Image Watermarking

The basic model of digital image watermarking consists of two parts; the first part is the watermark

embedding process which shown in Figure 1, and the second part is the watermark detection process

which shown in Figure 2.

Figure 1: Watermark Embedding

Figure 2: Watermark Detection

In Figure 1, which represents sender, the Watermark is embedded into the Cover Image with the

Secret Key that ensures the security of watermarking process. The output is the Watermarked Image.

In Figure 2, at the receiver side, the detector detects the watermark from the Watermarked Image by

using the Secret Key to recover the Watermark (Yusof & Khalifa, 2007).

3. Wavelet Watermarking Technique

Wavelet transform has been used widely since it has been adopted in the established image coding

standard JPEG 2000 (Yusof & Khalifa, 2007). In general, wavelet watermarking schemes excel in

terms of robustness and imperceptibility (Giakoumaki, Pavlopoulos, & Koutsouris, 2005), and it

produce considerably better quality for decoded image than JPEG. The main advantage that DWT has

over Fourier transforms is temporal resolution. It captures both location and frequency information.

The basic idea of DWT is to separate frequency detail, which is multi-resolution decomposition. One

time of decomposition can divide the image to four sub images at a quarter sizes. They are a low

frequency approximate sub image, and three horizontal, vertical, and diagonal direction high

frequency details sub images (Dubolia, Singh, Bhadoria, & Gupta, 2011).

Cover Image Watermarked

Image

Watermark

Embedding

Watermark

Secret

Key

Watermarked

Image Watermark

Watermark

Detection

Secret

Key


Figure: 3 Four level DWT

Figure 3 is an example diagram of four-level DWT decomposition. In the wavelet transform domain,

high frequency parts represent detailed information of image's edge, contour and texture and so on. It’s

not easy to detect the watermark in these places as people are not easily able to recognize it. But after

processing or attacking, it doesn’t have good stability. Most energy of image is centralized in low

frequency. Low frequency coefficients are nearly not changed by common attacks so that watermarking

information embedded in low frequency coefficients has better robustness (Dubolia et al., 2011).

4. Related Works

(Unser & Aldroubi, 1996) presented an overview of various uses of wavelet transform in medicine and

biology. It describes the wavelet properties that are the most important for biomedical applications. It

shows also the variety of uses of wavelet transform in biomedical applications. However, wavelets are

not a general solution for everything; they should be used with caution. The selection of a particular

solution should always be motivated by the problem itself.

(Nassiri, Latif, Toumanari, & Maoulainine, 2012) proposed a new watermarking scheme for grayscale

medical images. The scheme embeds a watermark in the low frequency part of the discrete wavelet

transform of a medical image. It uses domain wavelet transform, to ensure an authentication service

suitable for medical images to gray level fostering a fragile rather than robust of the watermark

imbedded. The performance of this scheme is examined by imperceptibility and robustness.

(Memon et al., 2009) proposed multiple watermarking algorithm. The algorithm embeds robust

watermark in region of non interest (RONI) to achieve security and confidentiality. While integrity

control is achieved by embedding fragile watermark in region of interest ROI. Since ROI in the medical

image is important in diagnosis process so it must be kept unchanged. In order to avoid the distortion

caused in ROI due to watermark imbedding process, original ROI data is first separated and embedded

outside the ROI. This will help in recovery of original ROI at the receiving end.

(Hyung-Kyo, Hee-Jung, Seong-Geun, & Jong-Keuk, 2005a, 2005b) presented a robust watermarking

for medical image that embeds the watermark with ROI information into the parts surrounding ROI

area of medical image. The watermark is the value of bit-plane in DWT of the decision area for

certification method of integrity verification.

(Lingjian, Qianjin, Feng, & Wufan, 2005) presented a novel fragile watermarking scheme based on the

integer lifting wavelet transform, it uses the quadtrees structures. It ensures the fragileness of watermark

and has strong localization ability for tampering due to its space-frequency characteristic. Moreover, it

can resist malicious attacks even if the algorithm and watermark are opened completely in the case of


introducing the secret key system. It is less complexity and more practicability. However, there are still

some unsolved technical issues for embedding procedure that include the secret key system and the

process of seeking relevant node. Therefore, further technical improvement for the proposed method is

needed.

(Irany, Guo, & Hatzinakos, 2011) proposed a high capacity reversible multiple data hiding scheme

based on integer-to-integer wavelet transform and histogram shifting. It uses a scalable location map

that size is significantly small comparing to the payload and it contains different stopping conditions on

both the level of the wavelet transform and the number of coefficients used in each sub band to achieve

high visible quality and multiple watermarking properties. The proposed scheme exhibits high image

quality at high embedding rates for medical images. It can be used to insert sensitive personal

information into generic files such as health records and medical images. It will eliminate the need to

store private data separately. However, it’s needed to design a robust reversible multiple watermarking

scheme that tolerates slight geometric modifications and lossy compressions.

(Kurniawan, Adiwijaya, & Agung, 2012) implemented Reed-Solomon code for robust watermark in

wavelet domain and SHA-256 for fragile watermark in Hash Block Chaining. The proposed multiple

watermarks can be implemented simultaneously on an image. Therefore, the integrity control and

authenticity of the image detection can be achieved at the same time.

(Pal, Ghosh, & Bhattacharya, 2012) presented a novel algorithm for medical image watermarking by

embedding several copies of the same information in the image by replacing bits in horizontal (HL) and

vertical (LH) resolution approximation image components of wavelet domain. The proposed scheme

use an approach for recovering the hidden information from the damaged copies due to unauthorized

change of data under attack by applying bit majority algorithm to reconstruct the information very close

to the original copy.

(Pan, Coatrieux, Cuppens, Cuppens, & Roux, 2010) proposed a new additive lossless watermarking

scheme which identifies parts of the image that can be reversibly watermarked and conducts message

embedding in the Haar wavelet transform coefficients. In addition, this approach used an approximation

of the image signal that is invariant to the watermark addition for classifying the image to avoid

over/underflows.

(Nambakhsh, Ahmadian, Ghavami, Dilmaghani, & Karimi-Fard, 2006) presented a novel blind

watermarking algorithm with secret key by embedding ECG signals in medical images. The original

image is compressed using the embedded zero-tree wavelet (EZW) algorithm. The extraction process is

performed at the decompression time of the watermarked image. The proposed algorithm is able to

utilize about 15% of the host image to embed the mark signal. This marking percentage has improved

previous works while preserving the image details.

(Sung-Jin, Hae-Min, Seung-Hoon, Yongwha, & Sung Bum, 2009) To improve transmission speed of

medical images between the hospitals, this paper proposes an algorithm that utilizes both JPEG 2000

and robust watermarking for protection and compression of the medical image. Thus, the medical

image should be compressed with JPEG 2000 by high compression ratio. With this algorithm, it takes

considerably less time to do JPEG2000 and watermarking than when they are done separately.

5. Multiple Watermarks Schemes

Some algorithms contain several watermarks for several purposes. First, a robust watermark is used for

doctor’s digital signature. Second, a caption watermark is used for patient’s personal and examination

information. In addition, an index watermark for image retrieval. Lastly, a fragile reference watermark

is used for integrity control.


(Giakoumaki, Pavlopoulos, & Koutouris, 2003) proposed a multiple watermarking scheme appropriate

for medical images, it addresses solving the problems of confidentiality and authentication of both

origin and data. The method uses Haar discrete wavelet transform to embed three types of

imperceptible watermarks into the wavelet coefficients: First, a robust watermark containing the

doctor’s identification code. Second, a caption watermark including patient’s personal information,

health history diagnosis reports. Lastly, a fragile watermark is used for tamper assessment.

(Giakoumaki, Pavlopoulos, & Koutsouris, 2004) proposed a multiple watermarking scheme that

addresses solving the problem of medical confidentiality, origin and data authentication, and image

retrieval. The algorithm uses Haar discrete wavelet transform to insert four types of watermarks into the

wavelet coefficients of an image: a robust watermark containing the doctor’s digital signature, an index

watermark including keywords for image retrieval, a caption watermark carrying patient’s information,

and a fragile watermark for integrity control.

(Giakoumaki et al., 2005) presented a wavelet multiple watermarking scheme, by applying Haar

discrete wavelet transform and a proper quantization of coefficients. It embeds four types of

watermarks into medical images, a robust watermark conveying the physician digital signature, an

index watermark carrying keywords to facilitate image retrieval, Moreover, a caption watermark

containing patient’s personal and examination data is embedded. Finally, a reference watermark is

embedded for data integrity control. This scheme aims to enhance protection of sensitive data, provide

source and data authentication service, and allow efficient image archiving and image retrieval.

(Manasrah & Al-Haj, 2008) applied a wavelet multiple watermarking scheme that aims to solve the

management issues of medical images by embedding three types of watermarks; a fragile annotation

(caption) watermark that has the highest capacity and contains patient's personal data, diagnosis, and

health history, a robust watermark for origin authentication (signature), and an index watermark which

includes the keyword for image retrieval.

(Badran, Sharkas, & Attallah, 2009) proposed a new multiple watermarks embedding scheme that

embeds four types of watermarks. This scheme extracts the ROI (region of interest) as a watermark to

be embedded twice: First, it’s embedded as a robust watermark in wavelet domain in the RONI (region

of non interest). Second, it’s embedded as a fragile watermark in spatial domain in the ROI. In addition,

multiple watermarks such as the physician's digital signature and EPR (Electronics Patient Record) are

embedded in the RONI in wavelet domain depending on a private key. The results showed that this

scheme is robust to ROI removal, JPEG compression, and some geometrical attacks; low pass and

median filters and some types of noise as Gaussian, Poisson, Salt and Pepper and Speckle. However,

this scheme needs to be more robust for some other attacks.

6. Integer Wavelet Transform

(Golpi, x, ra, & Danyali, 2009) proposed a blind reversible watermarking approach in medical images

by using wavelet histogram shifting. An integer wavelet transform is applied to map the integer host

image components to integer wavelet coefficients. The watermark information is embedded into the

high frequency sub-band areas of the transformed image.

(Zhuang, Zeng, & Li, 2008) proposed a fragile watermarking approach based on integer wavelet

transform and chaos sequence. The novel watermarking scheme uses the Quad tree structures received

by the wavelet decomposition and chaos sequences to choose locations of the embedded information.

The scheme has high Signal-to-Noise because it only embed watermark into the latest bit of node. The

difference between this approach and other approaches based on integer wavelet transform is that the

watermark is embedded in the coefficients of integer wavelet transform separately.


(Liu, Lv, & Wang, 2008) proposed a semi-fragile digital watermarking scheme for medical images

based on integer wavelet transform to estimate integrity and authenticity of medical images. Using

matrix norm quantization, watermarks are embedded into medium-frequency and high-frequency detail

sub-bands of integer wavelet domain of medical images. This algorithm achieves robustness and

sensitivity. It can also locate the tampered area exactly.

(Liu, Lv, & Luo, 2010) proposed a blind-testing watermarking scheme for medical images based on

integer wavelet transform to protect and estimate the integrity and authenticity of medical images. By

using matrix norm quantization, watermarks are inserted into low-frequency, medium-frequency, and

high-frequency sub-bands of integer wavelet domain of medical images. This algorithm achieves

robustness and sensitivity. It can also locate the distorted area exactly.

(Cheng-Ri, Dong-Min, Dong-Chul, & Seung-Soo, 2008) proposed a new fragile watermarking scheme

for medical images. This scheme makes it possible to resolve the security and forgery problem of

medical images. Integer wavelet transform is used to utilize hash function. The watermark associated

with the hash values is imbedded into the LSBs of the integer wavelet transform coefficients. This

algorithm can detect the forged area of the medical image effectively.

(Tashk, Danyali, & Alavianmehr, 2012) proposed a modified semi robust digital image watermarking

scheme for the purpose of tamper detection and recovery ability. The scheme has two stages. First, the

watermarked is produced by a specific order of the integer wavelet coefficient of the original image

which prepares both tamper detection and recovery ability. Second, watermark embedding and

extraction procedures are done. A Convolutional Error Correction Code is used in the watermark

establishment process be more robust. In the extraction process, the tamper will be detected and the

original image shall be recovered. This scheme is also enhanced to be adopted for reversible ROI

recovery of medical images even if there are some kinds of unintentional and intentional attacks.

7. Combining other Techniques with Wavelet

(Jingbing, Xianhua, et al., 2011) proposed a robust algorithm based on DWT-DFT, it has no restriction

of the number of watermarks. The part of sign sequence of DWT-DFT coefficients is used as feature

vector, which is used to enhance the robustness against rotation, scaling, translation attacks. In addition,

the content of medical image remains unchanged in this algorithm which is one kind of the zero

watermarking technology. This algorithm can easily embed multiple watermarks, where different keys

correspond to different watermarks. Moreover, the watermark can be extracted without the original

medical image.

(Jingbing, Yong, et al., 2011b) proposed a robust multiple watermarks of medical volume data using

3D-DWT and 3D-DFT. The part of sign sequence of 3D-DWT and 3D-DFT coefficients are used as

feature vector, which is utilized to enhance the robustness against JPEG compression, Gaussian noise,

Median filter, and cropping attacks. In addition, the content of medical volume data remains unchanged

in this algorithm which is one kind of the zero watermarking technology.

(Chunhua, Jingbing, & Yen-wei, 2012) proposed a robust multiple watermarks scheme for medical

images based on DWT and DCT, which can effectively solve the problems of how to determine the

region of interest (ROI), and improve the hiding capacity. The algorithm combines the visual feature

vector of images, encryption technology with the third party authentication, and avoids the boring

process for selecting ROI. This algorithm can enhance medical image security, confidentiality and

integrity in the application for the clinical.


(Kongo et al., 2012) proposed a new watermarking scheme, using another approach of wavelet

transform, classical (DWT) and dual tree transform (DT-CWT), using real and complex version;

combined with Bivariate Shrinkage function. The Bivariate function is applying, for estimating the

image at the extracted watermark step. The results of experiment showed that DT-CWT scheme has

better performance in comparison with DWT. The system is transparent to the user and allows image

integrity control. However, the watermarked image has some degradation, and at the receiver, the

watermark is not accurate enough.

(Soliman, ella Hassanien, & Onsi, 2012) proposed a novel application of Quantum Particle Swarm

Optimization (QPSO) for copyright protection and authentication. The watermark is embedded on

singular value vector (SVD) within low frequency sub-band in the hybrid DWT-DCT domain. The

main idea of this paper is to make a trade-off between the imperceptibility and robustness in medical

images.

(Jingbing, Chunhua, Mengxing, Yong, & Huaiqiang, 2012) proposed an algorithm that increase the

security of medical images when they are being transmitted through a wireless network. The scheme

uses a part of sign sequence of DWT-DCT coefficients as the feature vector of images to enhance the

robustness against common and geometric attacks. The watermarking image is scrambled by Arnold

transform to enhance its privacy.

(Jayanthi, Selvalakshmi, & Rajamani, 2009) proposed a normalization procedure, which is robust

against Affine transform attacks. This watermarking scheme is suitable for public watermarking

applications as the original image is not available in the watermark extraction. The watermarking

scheme employed as a direct-sequence code division multiple access approach in order to embed

multibit text information in DCT and DWT. The watermark in biomedical images is embedded in the

other regions than the area of interest so that the diagnosis is not affected, while the area of interest in

the biomedical image is found out using the K-means segmentation method.

(Jingbing, Yong, Wencai, & Yen-wei, 2011a) proposed robust multiple watermarks of medical volume

data using 3D DWT-DCT to embed multiple diagnosis results into one medical volume data. The part

of sign sequence of 3D DWT-DCT coefficients are used as feature vector, which is utilized to enhance

the robustness against some attacks such as rotation, scaling, translation attacks. Feature vector, Hash

function, and the third party authentication are combined in this algorithm, and different keys

corresponding to different watermarks.

(Nakhaie & Shokouhi, 2011) proposed a new No-Reference (NR) objective quality measurement

method based on spread spectrum technique and discrete wavelet transform using ROI processing. The

original is divided image into two separate sub-images called Region of interest (ROI) and Region of

non interest (non-ROI). ROI is the decision area in a medical image that is very important .This area

may indicate a disease, and the right diagnosis depends on this area. Spread spectrum embedding

algorithm is used to insert a binary mark into DCT transform of non-ROI part of image. At the receiver

side, ROI part is extracted with least degradation. Next, the watermark is extracted from non-ROI part

and a measure of its degradation is used to estimate the quality of the original image.

Conclusion

This paper proposed to cover recent works done on using wavelet transform on medical images. It

highlights the main applications of using medical images watermarking. Then it presents the technique

of wavelet transform watermarking. Next, it reviews some papers related with this topic. Finally, it

concludes this work.


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