EPR (Electronic Patient Records) hiding within medical images

1. Applying Information Security to Healthcare Services
A Review Study
Marwa Yousif
The advent of the internet and the widespread use of relatively low cost computing and
networking devices, make it possible for innovative ways of medical collaboration and
healthcare services provision through e- health and telemedicine programs. A need for an
effective healthcare information system, with reliable storage and secure ways to exchange
patient information arises. Reliable storage can be achieved by having complete medical
information of patients available in one consistent application and not in dispersed information
systems; that reduces cost and efforts of handling such information. Also, protection of the
Integrity and confidentiality of medical images has become an essential issue in the management
of patients’ medical records.
Data Hiding in Medical Images
Hiding EPR (Electronic Patient Records) data within medical images has been a dynamic
research area in recent years, because of its significance in telemedicine and computer- aided
diagnosis applications.
Steganography and watermarking techniques has been widely used in research and application
areas to hide EPR within medical images to reduce storage and information management cost for
health care facilities in an e- health environment and to ensure confidentiality and integrity of the
hidden data. Both of them aim at hiding data within signals. But whereas steganography aims for
imperceptibility to human eye and also high payload, digital watermarking considers the
robustness against removal a top priority.
Data hiding within images is categorized into reversible and irreversible methods. In the later the
distortion caused by data embedding is permanent and the original image can not be retrieved
from the stego image; while at the reversible data hiding methods the original image can be
retrieved without significant loss of information. Reversible data hiding methods are more suited
to medical images, as they are sensitive to the distortion that irreversible methods cause, which

2. may risk a proper diagnosis and illness treatment in an e- health environment. There are three
groups of reversible data hiding: data compression, pixel-value difference expansion (DE)
scheme, and histogram-based scheme [1].
Related Work
C. C. Chang, Z. H. Wang, and Z. X. Yin, have proposed a data hiding method employs the least
significant bits to embed secret data [2]. In essence, each color image is composed of red-green-
blue planes; their scheme embeds secret bits into one of the planes of the color image to enhance
the security of the transmitted message. The definition of the blood vessels plays a key role in the
quality of a retinal image, so they embed data in a Region of Interest (ROI) that exclude the
blood vessels and the background of the retinal image.
Their method is ingenious as they have described it in the title of their research; they have
managed to avoid distorting the pivotal region of the retinal image which is the blood vessels that
have got the diagnostic property of the retinal diseases and has a high clinical value, by
embedding the EPR in other less important regions of the Fundus image. Also, the method is
replicable by other researchers due to its simplicity and clarity. However, embedding such
sensitive data as the EPR within the least significant bits of an image introduces security and
integrity issues as the embedded data can be easily discovered and removed; least significant bits
are the first place that is expected by attackers.
Rajesh Barapate, Dr. Suresh Mali, and Dr. Dinesh Yadav have proposed a method that increases
the payload of the EPR data within medical images in [3]. It is a data hiding scheme within the
low and mid frequencies of the Discrete Cosine Transform “DCT” coefficients of a medical
image to allow for a higher degree perceptual quality of the stego- image and avoid distortion
that may result due to direct embedding in the spatial domain. Also, the scheme takes care of the
essential parts of the image that have the most important diagnostic features (Region of Interest
ROI) and embed the EPR in less diagnostically important parts.
Capacity (payload) is an imperative parameter for any data hiding scheme especially within
medical images, as it involves high volume of Electronic patient records “EPR” data. The
scheme addresses capacity by integrating a new coding technique called Class Dependent

3. Coding Scheme “CDCS”, which combines both variable and fixed length coding to get less
number of bits represent the same amount of information.
The scheme provide for high capacity of text EPR data, and also acceptable perceptual stego
image quality as the PSNR is equal to or greater than 40 DB; however, it does not provide for
high capacity of other type of EPR data such as images, which are common in the field of
telemedicine and EPR data.
Fatma E.-Z. A. Elgamal, Noha A. Hikal, and F.E.Z. Abou-Chadi have proposed a scheme that
allows secure medical images sharing over cloud computing environment [4]. Cloud computing
have introduced revolutionary business model in terms of cost and simplicity; however it has
also introduced challenges such as the integrity and confidentiality of the shared data that are
needed to be addressed in any cloud adoption especially in the field of telemedicine where
people health and lives are at stake.
The scheme implements two approaches in order to provide three security levels for the shared
medical image. The first is the authentication between the owner of the data and the destination,
the second is the authentication between the owner of the data and the cloud service provider,
and the third is the authentication from the destination of the data to its owner. As previously
mentioned, two approaches along with encryption were used to implement these security levels;
the first applies spatial watermarking technique that embed data within the Least Significant Bits
(LSBs) of the image, and the second uses a hybrid of spatial and DCT transform techniques.
The strength of this method comes from taking into account the cloud computing model which is
most likely the future preferred business model. However, the spatial embedding part of the
method utilizes the LSBs which considered the least secure method as it is the first place where
an attacker looks. Also, it uses only text data as the EPR in the experiments, while in practice,
EPR data may include other data format such as images. We do not know to which extent the
impeccability property would degrade if using images for example as EPR data. As much as this
research concerns, no experiment regarding the Fundus image, which considered one of the
most distortion sensitive medical image.
Sukhmeet Kaur Brar, and Gianetan Singh Sekhon have proposed a method that provides for both
a higher capacity and a better image perceptual quality [5]. Payload is increased by encoding the

4. EPR data using Class Dependent Coding scheme CDCS that allows representing the same
amount of information in less bits, and also by embedding in the LSBs of the image which
allows higher capacity as well. The method achieve less distortion to the host image by
embedding in the virtual borders using the modified difference expansion watermarking, in
which the size of the watermarked image is increased by two points in height which implies
embedding at Regions of Non Interest RONI (within the virtual borders).
In spite of the relatively high capacity, and the less distortion that the method brings, there are
limitations need to be considered when applying the methods for data hiding within digital
images. First, while increasing the capacity due to LSB embedding, the confidentiality and
integrity of both hidden data and the host image are compromised that the LSBs are the less
secure place for hiding data. Also, the dynamic size of the image is not maintained as it is
increased by 2 points in height which also compromises the confidentiality of the embedded
EPR.
Zhenfei Zhaoa, Hao Luoc, Zhe-Ming Luc, and Jeng-Shyang Pand have proposed a reversible
data hiding scheme based on histogram modification [6]. Their work based on modifying the
histogram of the differences between neighboring pixels- which are equal to or close to zero- by
the secret data. It is a multilevel histogram modification which leads to a higher capacity
compared to other one or two levels conventional histogram modification methods. Also, as the
differences concentricity around zero is improved, the distortions on the host image introduced
by secret content embedding is mitigated.
The method addresses capacity in terms of the multilevel histogram modification using more
peaks for data embedding; and also addresses imperceptibility and stego image perceptual
quality by using the histogram of the differences of neighboring pixels instead of direct
embedding in the host image’s histogram; however, the authors have compared the capacity and
imperceptibility of their method to other histogram methods only; from a marketing point of
view, why should a healthcare service implement their method given all other spatial and
transform domain available methods, they did not prove that their method is superior compared
to other non- histogram methods in terms of capacity and imperceptibility.

5. R.F. Olanrewaju, Othman. O. Khalifa, Aisha Hassan Hashim, Akram M. Zeki and A.A. Aburas,
have avoided in [7] the possible distortion caused by adding the hidden data directly to the host
image, by embedding an EPR watermark within the complex coefficients of the Fast Fourier
transform (FFT) of the host medical image (Fundus & Mammograms) using neural network, in a
method they entitled: Complex Valued Neural Network (CVNN). The method involves mapping
the watermark bits to the synapse weight of the host image instead of direct embedding to avoid
distortion or loss of information in the original image. It uses Complex neural networks to avoid
loss of information that the embedding is in both the real and imaginary parts of the host image.
Although they have proved that their method is distortion- free and also no loss of information
caused by embedding EPR data, which is a pivotal diagnostic criterion for medical images
specially the retinal ones, they have not addressed the capacity, which is also an imperative
parameter in today’s telemedicine environment.
Summary of Reviewed Methods
Method Description (Main Features) Medical Image Data Hiding Parameters
(Capacity, Imperceptibility, Confidentiality)
Strengths Limitations
An Ingenious Data
Hiding Scheme for
Color Retinal
Image
Tries to overcome the challenge
of embedding EPR data without
distorting the pivotal diagnostic
blood vessels within the retinal
images.
-Avoid the distortion of the
diagnostically sensitive blood
vessels by embedding in other
regions.
-Increased capacity due to
embedding within the LSBs of
the image.
The LSB embedding
compromises the
security (confidentiality
and integrity) of the
resulted image.
EPR Hiding in
Medical Images
with CDCS and
Energy
Thresholding
-Uses CDCS method to increase
capacity.
-Embed in DCT in low and mid
frequency coefficients.
-Capacity: Increased capacity
for Text EPR data.
-Imperceptibility: The Stego-
images gives PSNR value
more than 40dB
Capacity: Image EPR
data are not represented
in CDCS coding, so the
increased capacity for
only text EPR data.
Secure Medical
Images Sharing
over Cloud
Computing
environment
Two security approaches were
presented to guarantee a secure
sharing of medical images over
the cloud computing
environment by providing the
mean of trust management
between the authorized parities
of these data and also allows the
Security over the Cloud:
Takes into account the cloud
computing model which is
more likely to be the preferred
business model in the future
-LSB Embedding:
considered the least
robustness against
removal embedding
method.
-Uses only text data as
EPR, We do not know to
what extent the

6. Conclusion
It has been recognized that all the investigated methods tradeoffs between the capacity,
imperceptibility, and security parameters of a data hiding scheme. While some focus on capacity
and compromise security others focus on imperceptibility and compromise the capacity. Future
researches should focus on optimizing the three parameters so that we obtain an Image/ EPR
scheme that is secure, diagnostically reliable, and large enough to accommodate the ever
growing EPR data.
privacy sharing of the EPR. impeccability property
would degrade if using
images for example as
EPR data.
A Hybrid
Watermarking
Approach Using
Difference of
Virtual
Border and CDCS
for Digital Image
Protection
-uses a technique named Class
Dependent Coding Scheme
(CDCS) that allows less number
of bits to represent the same
information.
-embeds using modified
difference expansion
watermarking using LSB
replacement in the
virtual border technique which
implies embedding in RONI
-Increased capacity (CDCS
EPR & LSB embedding)
-Less distortion as the
embedding in RONI
-LSB Embedding: while
increasing the capacity,
the goal of the method, it
is compromising
confidentiality
The dynamic size of the
image is not maintained
as it is increased by 2
points in height
Reversible data
hiding based on
multilevel
histogram
modification and
sequential recovery
-This paper proposes a
reversible data hiding scheme
based on Histogram
modification.
-principle is to modify the
histogram constructed based on
the neighbor pixel differences
instead of the host image’s
histogram.
Higher capacity due to Using
multilevel Histogram
modification compared to one
or two levels histogram
modification methods.
- the capacity and
imperceptibility have
been compared to other
histogram methods
only.)
Distortion free
embedding in the
Optic Disk of
Retina Fundus
Images Using
Complex-Valued
Neural Networks
-This paper presents a
distortion- free method for
embedding data in the optic
nerves of the retinal images
(Fundus) in the Fourier
transform of the image.
-This is accomplished by
Complex- Valued Neural
Networks “CVNN” method.
No distortion or loss of
information caused by
embedding EPR data.
The main limitation is
that it excludes and
ignores capacity
(Payload).

7. References:
1- Li-Chin Huang, Lin-Yu Tseng, and Min-Shiang Hwang, “The Study on Data Hiding in
Medical Images”, International Journal of Network Security, Vol.14, 2012;
2- C. C. Chang , Z. H. Wang , and Z. X. Yin, “An Ingenious Data Hiding Scheme for
Color Retinal Image”, Proceedings of the Second Symposium International Computer
Science and Computational Technology (ISCSCT ’09) Huangshan, P. R. China, 26-
28,Dec. 2009;
3- Rajesh Barapate, Dr. Suresh Mali, Dr. Dinesh Yadav, “EPR Hiding in Medical Images
with CDCS and Energy Thresholding”, 1st International Conference on Recent Trends
in Engineering & Technology, Mar-2012 Special Issue of International Journal of
electronics, Communication & Soft Computing Science & Engineering, 2012;
4- Fatma E.-Z. A. Elgamal, Noha A. Hikal, F.E.Z. Abou-Chadi, “Secure Medical Images
Sharing over Cloud Computing environment”, (IJACSA) International Journal of
Advanced Computer Science and Applications, Vol. 4, No. 5, 2013;
5- Sukhmeet Kaur Brar, Gianetan Singh Sekhon, “A Hybrid Watermarking Approach
Using Difference of Virtual Border and CDCS for Digital Image Protection”,
International Journal of Emerging Technology and Advanced Engineering Website:
www.ijetae.com Volume 2, Issue 8, August 2012;
6- Zhenfei Zhaoa,b, Hao Luoc, Zhe-Ming Luc, Jeng-Shyang Pand, “Reversible data hiding
based on multilevel histogram modification and sequential recovery”, International
Journal of Electronics and Communications (AEÜ) journal homepage: www.elsevier.de/a
eue, 2011;
7- R.F. Olarnewaju, O.O. Khalifa, Aisha Abdulla, A.A Aburas, and A. MM. Zeki,
“Distortion- Free Embedding in the Optic Disc of Retina Fundus Images Using
Complex-Valued Neural Networks”, World Applied Science Journal, 2011.