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.
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 148
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
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 149
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
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 150
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.
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(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.
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(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.
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(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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