An Evolutionary Computing Enriched RS Attack
Resilient Medical Image Steganography Model for
Telemedicine Applications
Romany F. Mansour

Elsaid MD. Abdelrahim

Department of Mathematics
Faculty of Science, New Valley, Assiut University
Assiut, Egypt
romanyf@aun.edu.eg

Department of Mathematics
Computer Science Division, Faculty of Science,
Tanta University, Tanta, Egypt
elsaid_abdelrahim@yahoo.com

Abstract—The recent advancement in computing technologies
and resulting vision based applications have gives rise to a novel
practice called telemedicine that requires patient diagnosis images
or allied information to recommend or even perform diagnosis
practices being located remotely. However, to ensure accurate and
optimal telemedicine there is the requirement of seamless or
flawless biomedical information about patient. On the contrary,
medical data transmitted over insecure channel often remains
prone to get manipulated or corrupted by attackers. The existing
cryptosystems alone are not sufficient to deal with these issues and
hence in this paper a highly robust reversible image steganography
model has been developed for secret information hiding. Unlike
traditional wavelet transform techniques, we incorporated Discrete
Ripplet Transformation (DRT) technique for message embedding
in the medical cover images. In addition, to assure seamless
communication over insecure channel, a dual cryptosystem model
containing proposed steganography scheme and RSA cryptosystem
has been developed. One of the key novelties of the proposed
research work is the use of adaptive genetic algorithm (AGA) for
optimal pixel adjustment process (OPAP) that enriches data hiding
capacity as well as imperceptibility features. The performance
assessment reveals that the proposed steganography model
outperforms other wavelet transformation based approaches in
terms of high PSNR, embedding capacity, imperceptibility etc.
Keywords—Medical Image Steganography; Ripplet Transform;
Adaptive Genetic Algorithm; OPAP; Telemedicine.

I.

INTRODUCTION

The high pace emergence in technologies and associated
computing approaches have given rise to a broad horizon serving
an array of applications among which medical image
communication is one of the dominating one. In present day
health care system, biomedical image transmission has become
one of the inevitable needs to ensure efficient seamless and
secure data transmission over uncertain channels. Presently, the
telemedicine field has gained impressive momentum, which has

enabled efficient and timely diagnosis remotely. However, the
probable adversaries caused due to (image) information deviation
could not be ignored. In most of the cases, the key biomedical
image information is transmitted distinctly in a perceptible
manner. No doubt, such transmission often remains prone to the
attack within channel and hence there can be compromise
towards the secrecy of the patient’s key details. To maintain
seamless medical image communication, Digital Imaging and
Communications in Medicine (DICOM) has developed
significant standard for medical data storage, information
extraction, printing and transmission over channels. The efficacy
of DICOM has enabled it a potential model for medical data
communication. No doubt, DICOM files can be easily
communicated between two parties which are authorized and
capable of receiving image and associated patient details in
DICOM format. However, DICOM doesn’t have any specific
security feature, as it merely stores the data in unqualifiable (or
non-editable) manner. On the contrary, there are many
application scenarios, where images originated from medical
examinations might require sharing inadvertently across
stakeholders, thus exposing significant information. This key
information could be accessed by any party without having
access right and this as a result could cause misuse. Interestingly,
in telemedicine applications ensuring intact image attribute and
key information is inevitable. In such applications retaining
image information intact during transmission throughout channel
is must. With this motivation, a number of efforts have been
made to enable data security during transmission. However, with
specific medical image information security data hiding or data
embedding approaches have gained widespread attention.
Various data hiding approaches such as watermarking,
cryptography or cryptosystems and steganography have been
exponentially applied to secure the data. Among these dominant
technologies, steganography is more efficient and suitable for
critical data security over uncertain channel conditions [1].
Steganography deals with hiding critical information onto the

cover image, which is then followed by transmission over
unsecured channel [1]. The secure information or secret message
could be text, audio, video, or image data. The robustness of
steganography enables it to be applied for various medical
imaging purposes, watermarking, inno-cipher, Geo-Static
Information (GSI) etc; where medical imaging system deals with
hiding significant patient related information in allied medical
images like X-Rays, Ultrasounds, CT scans, and MRIs.
Ensuring optimal steganography demands imperceptibility [2]
that comprises the following:
Invisibility-Since, the efficacy of steganography lies in the
ability to be unnoticed, invisibility is stated to be the first and
foremost inevitable need of the steganography technique.
Capacity-Steganography intends to ensure optimal data
security while ensuring higher embedding (secret message
volume) capacity. Typically, it is measured as bits per pixel (bpp)
unit.
Resilience against Statistical attacks and manipulation-It
signifies the volume of alteration the stego medium or the cover
image can endure or resist before certain adversary could
obliterate the embedded or the hidden critical information.
To perform data hiding in cover images or medical images,
numerous efforts have been made [3]. These approaches are
broadly classified into two types of techniques, spatial domain
and frequency domain techniques [4], where the first approach
embeds the secret data directly in to the least significant bit
(LSB) of the cover image, while in the later approach the secret
information is embedded in the LSB of transform coefficients.
Unfortunately, in the time domain approaches the quality of the
stego images often gets distorted and thus making it vulnerable to
get attacked due to perceptibility issues. On the other hand,
frequency domain approaches are confined due to low resilience
towards attacks [5]. Perceptibility caused due to embedding is the
prime reason behind such limitations, and hence to deal with it,
authors [6] suggested optimal pixel adjustment process (OPAP)
based LSB embedding technique. This technique [6] exhibited
better stego-image quality with relatively lower computational
complexity. Observing the fundamental process of medical data
security, especially by means of steganography, ensuring
minimal perceptibility and higher embedding capacity is must,
and to achieve it enhancing image transformation for message
embedding is must. Unlike major existing approaches, where
wavelet transforms have been applied, in this paper, the ripplet
transform (RT) technique has been applied that plays vital role in
retrieving or selecting most significant coefficient for data hiding.
In addition, it can alleviate the existing issues of the general
discrete wavelet transform (DWT) called two dimensional (2D)
singularity problem [7]. Being a reversible steganography, we
apply inverse discrete ripplet transform (DRT) for data extraction
at the receiver end. One more significant novelty of the proposed
work is the use of enhanced evolutionary computing (EC)
scheme called Adaptive Genetic Algorithm (AGA) for block
mapping to be applied for OPAP purpose. This as a result not

only assures imperceptibility but also ensures higher embedding
capacity. One of the key novelties of the presented research work
is the implementation of dual security model (DSM) where in
addition to the enhanced DRT based steganography; RSA
cryptosystem has been applied to secure the data access. The
efficacy of the proposed approach has been investigated in
conjunction with statistical attack assessment over stego image,
where it has exhibited higher PSNR, low entropy, minimal or
even negligible perceptibility and histogram variations.
The other sections of the presented manuscript discuss the
following. Section II discusses the related work, which is
followed by the discussion of the proposed discrete Ripplet
transform (DRT) Type-III and its application for enhanced
steganography technique for medical data security. This section
also discusses the implementation model proposed and associated
technologies such as DRT based image transformation and data
hiding, AGA based block mapping and OPAP, RSA
cryptosystem based dual security model. Section IV discusses the
results and analysis and the overall conclusion and future scopes
are presented in Section V. References used in the research work
are presented at the end of the manuscript.
II.

RELATED WORK

This section presents some of the key literatures discussing
medical data security using steganography (say, medical image
steganography (MIS) and various associated technologies.
In recent years, the significance of telemedicine has been
realized globally and hence it has emerged as one of the
dominating research domains across academia-industries.
However, to ensure seamless and flawless processes, maintaining
optimal medical data security is must. Steganography, being one
of the most efficient approaches for medical data security applied
image transformation schemes to embed critical data embedding
within cover image (i.e., medical image). In fact, the efficiency of
reversible steganography techniques significantly relies on the
efficacy of image transformation, data embedding, and pixel
adjustment to enable maximum imperceptibility. In majority of
the existing approach integer wavelet transform techniques
(IWT) or wavelet transformation approaches have been applied
[8][9][10][11] for steganography. In [8], authors developed arealtime data embedding model using IWT technique where it was
applied in the transform domain. Being an image compression
based approach authors have applied IWT as it outputs in the
integer form and hence consumes low memory space. In [12],
authors applied IWT based steganography for medical image
security. Authors focussed on converting multiple medical
images into single one where the cover image was processed wit
left-flipping and a dummy cover image was generated. Authors
considered the patient's medical diagnosis image as the secret
image and to retrieve the scrambled image they applied Arnold
transform. In process, the scrambled medical diagnostic image
(i.e., secret image) was embedded into the dummy cover image
and IWT was applied to obtain the dummy secret image. In the
next phase, authors fused the cover image with the dummy secret

image to obtain stego-image. However, the computational
overheads of such techniques can’t be ignored.
Authors in [10], derived a reversible data hiding or critical
data embedding approach using compressive-steganography
technique. Authors applied wavelets transform technique to
exhibit data embedding; however could not address the distortion
caused due to compression and its affect on the hiding capacity.
To further enhance compression based reversible steganography
for medical data security over uncertain transmission channel, a
low distortion reversible data hiding scheme was developed in
[13], where authors compressed a fixed section of the signal,
which is prone to get distorted. Tian [11] applied pixel value
difference expansion approach to enable a high capacity
reversible data embedding for image steganography. However,
authors could not address the aftermath consequences leading
vulnerability towards attacks. Similarly, in [14] [15] a Difference
Expansion (DE) embedding model was applied where authors
exploited HAAR wavelet transform by using horizontal as well
as vertical difference images to perform secret data hiding.
Authors [15] found that DE along with sophisticated location
map with enhanced expandability could achieve higher
embedding capacity. With goal to exploit histogram based
approaches for steganography, authors [16] applied a histogram
shifting approach that shifts (by one pixel) a fraction of the
histogram parts in between the peak and the zero level to the
right direction. This as a result created an empty bin in
conjunction with the peak point where they hide data. Similarly,
in [17], a histogram modification model was incorporated where
authors modified histogram on pixel differences that similar to
[16] forms sufficient place to hide data. However, these
approaches could not address the attack conditions, especially
statistical attacks in channel.
Authors [18] focused on medical data confidentiality issue
through steganography, where the cover images was at first
transformed into one-dimensional sequence by means of Hilbert
filling curve, which was then processed for splitting into nonoverlapping clusters of three pixels in each. To enrich
imperceptibility, authors applied adaptive pixel pair match
(APPM) based data embedding, where pixel value differences
(PVD) of the three pixels individually is retrieved and data is
embedded in those pixel ternaries. This as a result causes low
distortion and hence high imperceptibility.
Considering efficacy of LSB embedding, authors [19]
developed a LSB matching model, where data hiding was
performed in the edges and the pixel bits of the secret message
were matched with the LSB plane. In [20] a fuzzy logic-based
steganography model was proposed for medical diagnostic image
security. Authors applied random LSB selection based approach
to hide the secret data. Authors applied personal data and the
diagnostic suggestions as the secret information that was
compressed and encrypted to enable attack resiliency. However,
this approach could be found complicate. In [21] authors derived
a data hiding model for 3D MRI images, where at first they
applied segmentation to perform region localization followed by

LSB embedding. Authors [22] applied two-phase contourlet
transform technique for data hiding, where at first the cover
image was split into non-overlapped blocks and the secret
information was embedded in the high frequency component. In
addition, they embedded the secret data in the low frequency
component of the global contourlet transform. Authors [23]
derived a digital steganography model to hide Electronic Patient
Records (EPR) into medical diagnosis images without making
any significant modification in the image part. Realizing the fact
that the human visual system (HVS) is less sensitive to changes
in high contrast image sections, Authors [23] exploited edge
detection technique to recognize and embed secret data in spiky
image-parts. They applied Hamming code to embed three distinct
secret message bits into 4 bits of the cover image. Strategically,
to safeguard the decision area or region of interest (ROI), authors
embedded EPR into the Region of Non-Interest (RONI). A
similar effort was made in [24] where authors explored Pixel
Value Differencing (PVD) so as to localize contrast regions
which was then processed with Hamming code based data
embedding as discussed [23].
Majority of the approaches, particularly image transformation
approaches have applied standard wavelet transform techniques
to perform data embedding in image steganography. However,
realizing the singularity problems, most of the approaches are
found confined. To deal with this, authors [25] applied Ripplet-II
transform by performing high dimensional generalization of the
ridgelet transform where they incorporated a new variable called
degree (d). However, they focussed on medical image
classification and hence could not be explored for its efficacy to
enable reversible steganography. However, they found that RT
can perform better feature extraction or image transformation that
could enable an array of enhanced computations. A similar
outcome was realized by Dhaarani et al. [26] who examined its
efficacy for grey scale medical image compression. However,
they [26] obtained RT by generalizing the curvelet transform. To
enhance performance, authors [26] applied RT to signify the
images or 2D signals at distinct scales and orientations and
applied Huffman coding to encode significant coefficients.
Authors [26] found that RT exhibits satisfactory to achieve better
compression ratio and low mean square error. In [27] RT
technique (Ripplet Transform Type-I) was exploited significantly
to enable multimodality Medical Image Fusion (MIF). Authors
derived Ripplet Transform Type-I (RT) in conjunction with
Pulse-Coupled Neural Network (PCNN). Authors found that
Ripplet Transform can be a better alternative to perform image
decomposition [26][27] that eventually could play vital role in
medical image steganography.
Literatures reveals that applying evolutionary computing
techniques such as genetic algorithm (GA) can be effective for
steganography, particularly for embedding capacity optimization
and imperceptibility [28-30]. As empirical study, authors [29]
applied GA for steganography encoding on the JPEG images for
its security. GA was further applied in [30], where it was used for
block mapping in LSB embedding. In [31] a GA based
steganography was developed, where authors applied discrete

cosine transforms (GASDCT) and GA. Authors found that the
use of GA enabled lower bit error rate. Applying standard DWT
based image decomposition and GA based mapping, authors [32]
developed a steganography model that embeds data into DWT
coefficients, enriched with OPAP based optimization. Recently,
authors [33] derived a steganography model for biomedical
purpose. They applied queue data structure for communication,
where at first they encrypted secret message by means of Rabin
Cryptosystem that eventually provided multiple blocks and subblocks to be distributed equally. In their approach, the receiver
had four distinct values for plain text related to the single cipher
text so that only authorized receiver could retrieve the original
medical data. In [34] authors developed an encryption assisted
with LSB embedding model for patient information hiding in
medical image. A similar effort was made in [35], where authors
applied AES encryption over secret information to be embedded
into cover image. However, authors applied discrete cosine
transform (DCT) to perform image decomposition and hiding.
Further, in [36], authors exploited RSA and wavelet transform to
perform Hash-LSB based data embedding steganography.
This is the matter of fact that a number of researches have
been done to perform data embedding in images/medical image
using steganography techniques; however most of the approaches
use wavelet transform technique and are focussed on either
PSNR enhancement or embedding capacity enhancement.
However, the key requirements, such as ROI preservation,
maximum imperceptibility, minimal or negligible histogram
variations, statistical attack resilience, higher PSNR etc haven’t
been considered. Considering these as motivations, in this paper a
novel Ripplet Transform, LSB embedding, Adaptive GA based
OPAP, RSA based security key encryption has been developed
(dual level biomedical image security). The discussion of the
proposed steganography model is given in the following sections.
III.

OUR CONTRIBUTION

With continued research and improvement in algorithm
design, steganography can be taken as a serious means to hide
data and the present work appears that it was more efficient in
hiding more data. Unlike traditional IWT based approaches, this
paper applies Discrete Ripplet Transformation Type-I (DRT
Type-I) -algorithm to perform image decomposition followed by
secret message (i.e., patient information and previous diagnosis
details, doctor details). Being a LSB embedding approach, we
have applied an enhanced evolutionary computing approach
called Adaptive Genetic Algorithm (AGA) to perform block
mapping that enables not only the error reduction (between stego
image and the cover image), but also preserves regional image
properties by means of optimal pixel adjustment process. This
process
achieves
higher
embedding
capacity
and
imperceptibility, thus enabling an optimal solution for biomedical
image communication over uncertain channels. Furthermore, the
use of AGA based block flipping strengthens the statistical attack
(for example, RS-attack) resiliency. To alleviate the issue of RSattack on the stego image throughout channel, the impact of the
inter-relation between pixels is compensated by using AGA that

estimates the optimal adjusting mode for which the image
imperceptibility could be maintained optimal. In our proposed
AGA based block-mapping function, the, a chromosome is
encoded as an array of 64 genes comprising permutations [1 to
64] that signifies number of pixels in each block. Thus, in each
block, the data is embedded on multiple bites of the individual
pixel. One of the key novelties of the proposed steganography
model is the use of dual-security feature, where the secret
information are at first encrypted with RSA cryptosystem, which
is them followed by LSB embedding in the cover image. Here, in
LSB embedding, our model embeds or hides information by
replacing more than one bit from each pixel of the cover image.
Being a reversible steganography technique, the developed model
performs data embedding at the transmitter end, while extraction
is performed at the receiver, where inverse DRT Type-I is
applied to extract embedded secret information (i.e., patient
details, diagnosis details etc). To examine the efficiency of the
proposed steganography model biomedical data security for
telemedicine purpose, the performance has been examined in
terms of peak signal to noise ratio (PSNR), entropy, histogram
deviations, R and S parameters etc.
IV.

SYSTEM DESIGN

The overall proposed biomedical image steganography
(MIS) model comprises four phases:
1. Secret Patient Information Embedding,
2. GA Based Optimal Pixel Adjustment Process (OPAP),
3. Information Extraction,
4. RS Stegnalysis for DRT enriched Medical Image
Steganography (MIS).
A brief discussion of the proposed research phases is
presented as follows:
A. Secret Patient Information Embedding
Since, the proposed steganography model contains patients
information containing disease details, diagnosis made, doctor’s
details etc are in text which is further embedded to the
biomedical diagnosis image (say, cover image); our proposed
model can be stated as a multi-modal steganography. The overall
proposed steganography model is presented in Fig. 1. As
depicted in the following figure (Fig. 1), it can be found that the
proposed system at fist accepts the color biomedical image as
cover image, where the text information containing patient’s
details, diagnosis details etc are to be embedded. As stated, in
this research an additional security feature has been proposed to
enable a dual-security feature based biomedical data
communication, the secret key information is at first processed
using RSA cryptosystem algorithm. To ensure this additional
security feature, the user defined security key has been
considered, which is supposed to be shared with the recipients to
avoid decoding during transmission. Now, to perform secret data
embedding, unlike traditional integer wavelet transform (IWT)
algorithms, we have derived an efficient Discrete Ripplet

Transformation (DRT) algorithm to decompose medical cover
image into 8x8 blocks.
Start
Select Cover
Image (Biomedical
Image)
Select Patient’s,
Diagnosis details
and Doctor details)
Apply
RSA
Encryption
Divide cover image
into 8x8 blocks
Apply Mapping
function

Perform DRT

Apply LSB
Concatenate all
coefficient together
Evaluate fitness
function

Store the
coeff in new
image

Apply OPAP
4 Sub-bands
Apply
inverse DRT
Generate 64 genes

Pixel info

Generate
Stego
image
Stop

Fig. 1. Secret Patient’s information embedding

Before discussing the message embedding, the following subsection briefs the discrete Ripplet transformation (DRT)
technique and its efficacy over traditional wavelet transformation
approaches to enable optimal data embedding and
imperceptibility.
1) Ripplet Transformation
To achieve imperceptibility, maintaining efficient
representation of the biomedical image is must. In addition, to
achieve an optimal steganography, maintaining higher image
quality, embedding capacity (also called payload capacity),
minimal computational complexity etc is the inevitable need.

These all objectives can be achieved by means of certain efficient
transformation technique. However, the generic transformation
approaches such as Fourier transformation, wavelet
transformation techniques do suffer from various issues such as
discontinuities like edges in biomedical image. This as a result
can degrade the significant image information, and thus creating
ambiguity towards effective diagnosis. To alleviate such
limitations, in this paper, we have derived a new image
decomposition model using Ripplet Transform (RT), which can
be considered as a higher dimensional generalization of the
curvelet transform, especially derived for presenting image at
distinct scales and directions. It enables subjective support c and
the degree d that establishes a dominator of standard curvelet
transform. The acceptability of this model gets increased
significantly when it has to be applied with biomedical processes,
where there could be significant image information distributed
across image. On the contrary, the other approaches such as
Fourier transform technique can merely facilitate presentation of
certain smooth images, is unable to preserve quality of image
containing edges. Typically, edges introduce singularity problem
or discontinuity in the intensity of the images. On contrary,
presenting singularity effectively in medical image is a highly
intricate problem, particularly for harmonic analysis. According
to Gibbs phenomenon, the 1D singularity often leads destruction
of the sparsity of the Fourier series presentation of a function.
Interestingly, unlike Fourier transform, wavelet transform
techniques can represent certain function with 1D singularity [37,
38]; however it is unable to deal with the issue of 2D singularity
problem. This is because; standard 2D WT is a tensor product of
1D WTs that solves the 1D singularity in horizontal as well as
vertical direction. To deal with such limitation, authors [39, 40]
developed a ridgelet transform technique which is capable of
resolving 1D singularities in any random direction. Being
founded over Radon transform [41], Ridgelet transform provides
significant information about orientation of the linear edges in the
image that eventually enables extraction of the lines of arbitrary
orientations. As ridgelet transform too was found confined to
alleviate 2D singularity issue, authors [42-45] developed a
curvelet transform technique that exhibited solution along smooth
curves.
In general, Curvelet transform applies the concept of
parabolic scaling so as to achieve anisotropic directionality that
eventually gives hope to resolve 2D singularity problems along
curves [46, 44, 45, 47]. However, the universal efficacy of
parabolic scaling often remains questionable for different
boundaries conditions, which is common in case of medical
image data. In addition, the optimality of scaling concept is also
not justified so far. Therefore, to deal with such issues, Ripplet
transform has been derived that generalizes curvelet transform by
incorporating two additional attributes, support c and degree d.
These additional parameters, c and d facilitate RT with
anisotropy ability to represent singularities along an arbitrary
curve. Some of the key strengths of the RT approach are, multiresolution, good localization due to frequency domain support
and fast decay rate in spatial domain, hierarchical support for

image presentation, better directionality, higher scalability,
anisotropic efficiency and swift decay rate of the coefficients
(that results into higher energy concentration). Considering
biomedical image processing and key fundamental requirements,
in this thesis RT technique has been applied to perform cover
image decomposition and further coefficient embedding to hide
secret information using LSB embedding approach.
As already stated, curvelet transform applies parabolic scaling
concept to retrieve the properties of anisotropic directionality.
This ability resolves singularity issue along
curves. On the
other hand, RT resolves singularity problem by presenting image
with the discontinuities . Here, we incorporate two parameters
s, and d to generalize curvelet transform.

(6)
where
and
.
The frequency response of the RT (in transform domain) is
obtained using (7):
(7)

The DRT of the 2D image (signal)
having dimension
is usually presented in terms of the RT coefficients,
given by
. Mathematically,
(8)

Mathematically the derivation of the RT approaches is
discussed as follows:
2) Continuous Ripplet Transform (CRT)
Typically, continuous ripplet transform (CRT) is stated as the
inner product of 2D integral functions
and ripplets
,
which is mathematically presented as (1).

Finally, the image
inverse DRT given as (9).

is reconstructed by applying
(9)

(1)

where
signifies the RT coefficient and the other
parameters, , , and are the scale position, and the rotation
parameters. Mathematically, the Ripplet in transform domain
can be expressed as (2)
(2)

where
refers the FT of ripplet, and and signify
the radial and angular parameters, respectively. The other
parameters,
and
signifies the radial and the angular
windows, respectively. The introduced two variables
and
signify the degree and the support, correspondingly. To perform
inverse CRT, the following expression is used:
(3)

Now, considering our proposed steganography and associated
secret information hiding mechanism, at first the DRT algorithm
is applied over the cover image (i.e., biomedical image) and the
ripplet coefficients are obtained, which is then followed by secret
information hiding using LSB embedding approach. The
proposed secret embedding approach is presented as follows:
4) DCT Based Secret Message embedding
The sequential implementation of the DCT based secret
message embedding is presented as follows:
Step-1
Step-2
Step-3

Step-4
3) Discrete Ripplet Transform(DRT)
Unlike CRT, the discrete Ripplet transform (DRT) is
performed by discretising the RT coefficient
. In DRT,
scale parameter is discretised at dyadic intervals while other
remaining parameters
and
are discretised at equal-spaced
difference or intervals. These parameters are replaced with
respectively and thus, the following are obtained:
(4)
(5)

Step-5

Obtain the Biomedical Image data and apply it as
the cover image
.
Obtain the patient’s information, diagnosis, and
doctor details as secret data
.
Implement histogram modification as preprocessing over biomedical image (or cover
image).
Perform decomposition of the pre-processed
cover image using DRT and obtain the DRT
coefficients using (10).
(10)
To apply least significant bit (LSB) embedding to
hide the secret patient details or the secret
information.
In this process, the DRT coefficients are selected
by sorting it in increasing order and thus the
significant DRT coefficients are retrieved.
Interestingly, with the sorting approach, it raises
the bit-ordering issue that as a result introduces
un-embedding capacity and hence degrades

Step-6

Step-7

payload capacity. In this research work, to
alleviate such issue, a novel enhanced
evolutionary computing (EC) algorithm named
Adaptive Generic Algorithm has been developed
that eventually retrieves the optimal scrambled
DRT coefficient position at each block. In other
words, the proposed AGA model performs block
flipping so as to obtain the optimal coefficient
position where the data is hidden.
Once performing scrambling of the DRT
coefficient, the orientation
having the
maximum energy (or) the highest variance at the
individual block (
) are considered as the
significant coefficient to embed or hide the secret
information. Mathematically,
(11)

Now, the retrieved DRT coefficients are updated
to a matrix
for mth block.
(12)

discussed in the next section.
Once receiving the stego image at the receiver end, the
inverse DRT (IDRT) is applied over the stego image to retrieve
cover image. In addition, being RSA encrypted, our proposed
model requires recipient to use the same key applied for
encryption, so as to retrieve the secret information for decision
process.
B. Genetic Algorithm Based Optimal Pixel Adjustment Process
(OPAP)
The predominant objective of OPAP is to reduce the error in
between the stego image and the cover image (i.e., medical
image). For illustration, in case the pixel index of the cover
image is 10000 (signifying 16 as the decimal number) and the
secret data vector to be embedded for 4 bits be 1111, then it
would result into the change in the pixel index and would
become 11111 (characterizing 31 as digital number). In this case
the embedding error would be 15, while once implementing
OPAP the fifth bit is targeted to be made 0 (from 1 to 0) and thus
the error would be minimized to 1. The overall OPAP model can
be characterized into the following approach:
Case

1

Case 2
Case 3
In addition, to avoid perceptibility problem and statistical
attack issues such as RS-Attack, we have incorporated a
novel AGA based OPAP optimization model that estimates
the optimal pixel differences so as to enable higher message
or payload embedding capacity and low entropy.

Here, the variables
represents the associated
pixel values of the th pixel in the three images; cover image,
stego image and the retrieved image by the generic LSB
embedding process, respectively.

Now, obtaining the coefficient matrix the column
elements are put into the matrix .
(13)

In above expression, the variable
signifies the
embedding error in between the stego image and the cover image
[6]. Thus, once embedding the n-LSBs of the cover image with
n message bits, would be (15):

Step-8

where, , variables signify row and column of the cover
image or the biomedical image.
Now, the patient’s diagnosis details or the
considered secret data
is
converted to secret data that appends
supplementary security feature (RSA encoded data)
to the proposed dual security based steganography.
Step-10 Thus, the secret data
is
embedded as per the following expression:
(14)
where, signifies the scaling factor which we have selected
as
. Thus, with embedded data the stego image is
formed which is transmitted over network.
Step-9

Step-11

Now, perform optimal pixel placement algorithm to
avoid any statistical attack such as RS attack and to
strengthen imperceptibility. The discussion of the
proposed RS-Resilient block flipping and OPAP is

(15)
In this paper, Adaptive Genetic Algorithm, an enhanced
evolutionary algorithm with adaptive GA parameter update
(crossover probability and mutation probability) capability, has
been applied to perform OPAP. Here AGA obtained the
mapping function for each image blocks. In our proposed OPAP
model, a chromosome is encoded as an array of 64 genes
possessing the permutations 1 to 64 that signify indicate the
number of pixel in individual block. GA obtains the best
adjustment matrix. In the proposed work, PSNR is considered as
the fitness value of the GA based optimization, where after each
iteration or generation we expect to reduce the PSNR (16).
(16)

where
and
represent the image sizes, while and
signify the intensity before and after message embedding.

C. Information Extraction
In our proposed model, once receiving the stego image at the
recipient terminal, at first it is processed for inverse DRT where
the LSB coefficient from DRT coefficient (in coefficient domain
with coefficients with each
blocks) is selected, which is
then placed in a matrix
. It is then processed for descrambling
so as to substitute the coefficient in its own-place and the pixel
sequence is obtained. In this model, we estimate a correlation
estimator , which signifies the mean correlation in between the
individual row off is obtained by (17)
(17)

image steganography technique to deal with RS type attacks in
the channel. This section discusses the RS Stegnalysis and RS
resilient MIS and a novel AGA based RS resilient MIS model for
secure medical data communication over uncertain channels.
In the process of RS steganalysis, three distinct types of block
flipping named, positive
, negative
and Zero-flipping
are performed. The positive flipping
refers the
transformational inter-relation between
and
pixel,
which is equivalent to LSB. On the other hand, the negative
flipping
refers the transformational inter-relation between
and . Thus, the inter-relation between these two distinct
flipping can be derived as:
(19)

Thus, the patient data transmitted through medical image is
obtained by equation (18) and the medical data transmitted is
reconstructed by means of the inverse DRT.
(18)
In addition, to retrieve the accurate message data or the
patients’ information, diagnosis and doctor details are obtained
by applying decryption key which is the same as used for
encryption at the transmitter terminal.
The overall process of the information extraction is presented
in Fig. 2.

(20)
Let, ,
and
be the flipping functions. The flipped
group is obtained by means of executing these flipping functions
on each pixel of the medical image block. Mathematically, the
flipping group
is presented as
(21)
In (21), the variable
flipping mask, where
The parameter is regular in case of
G is singular if and only if

Select StegoImage
Apply LSB
Decryption
key

Split cover image into
8x8 blocks

Here, an identity permutation is defined in terms of Zeroflipping.

Actual
Secret text
extracted

Extract Ripplet
coefficient

Find pixel sequence
Fig. 2. Patient information (secret message) extraction

D. RS Stegnalysis Enriched OPAP for Medical Image
Steganography
Undeniably, there has been different type of attacks
functional on the basis of statistical correlation between pixels.
On the other hand, realizing the fact that embedding text
information or the patient’s information inside cover image could
lead entropy and thus making image perceptible. To avoid this
issue, in this paper, we have enriched our proposed medical

signifies a
, and
.

To perform RS Stegnalysis, at first, the medical image is split
into multiple non-overlapping blocks, where the individual is rearranged to form a vector
in certain
arbitrary order. Here, the inter-relation or the correlation between
pixels can be estimated using equation (22).
(22)

where refers the pixel value, while
number of pixels.

presents the total

On the other hand,
signifies the spatial correlation in
between the adjoining pixels. Here, smaller refers stronger
correlation. Once estimating
, non-negative flipping and nonpositive flipping are applied on the individual block. It is then
followed by the use of (22) to estimate the
in individual
block. Let
and be the relative number of the regular blocks
and singular blocks after positive flipping. Similarly,
and
be the relative number of regular and singular blocks after
the negative flipping. It is found that there exist a relationship
between these blocks in the following manner:

Interestingly, the difference usually increases between the
relative number of the regular blocks
and
increases as
per the size of the message embedded into the image. Similar
feature is observed for the difference between singular blocks
and
.
1) RS Steganalysis Resilient Medical Image Steganography
On the basis of aforementioned logical discussion, in the
proposed AGA based RS Stegnalysis model the pixel values are
adjusted so as to maintain
. Since,
embedding patient’s details or secret message information into
the biomedical image data alters the bits in higher planes and that
consequently degrades the imperceptibility of the stego image,
merely the 2nd and 3rd lowest bit-planes are altered. For
illustration, let
be the initial value of an image block and
considering the alteration in the second lowest bit-plane, the
variations incorporated between the initial (i.e., original image
block) and altered block can be visualised as an adjustment
matrix such as
or . Let the altered image blocks be
. For illustration, for the initial or the
original block
, where
signifies the non-positive flipping. Now, for altered
block
, only when
is non-positive
flipping. Similarly, in case of other altered block
. These all illustrations state that the kind of the
block (regular or singular) can be easily changed by means of
certain effective adjustment. This as a result can ensure that RS
steganalysis could not detect the presence of certain hidden
message or text within the stego image.
To deal with the RS attack in channel, in this paper, we have
derived proposed steganography model as RS resilient model,
where AGA has been applied to estimate the optimal adjustment
matrix. AGA transforms an optimization in the form of
chromosome evolution phenomenon. Once retrieving the optimal
solution (i.e., individual chromosome with expected fitness
value), it gives optimum or sub-optimum solution. Similar to the
above mentioned adaptive genetic algorithm, to incorporate RS
resiliency, we have varied crossover and mutation probability
adaptively where these value varies as per the chromosomes
having similar fitness value. It reduces not only the
computational overhead and time, but also avoids local minima
and convergence type issues, which is common in major standard
learning approaches including GA and neural networks. In this
model, once embedding the patients information inside the DRT
coefficients using LSB embedding approach, AGA has been
applied to perform adjustment (say enhanced OPAP).
In this process, the stego image is split into
the obtained blocks are labelled as follows:
1.

2.
3.

blocks and

For block B, implement non-positive and non-negative
flipping on the block and generate the flipping mask
and
, randomly to obtain
and , respectively.
Estimate
and
.
Declare four variables to classify blocks by comparing
.



, event count when the block is regular under
non-negative flipping.

, event count when the block is singular under
non-negative flipping

, event count when the block is regular under
non-positive flipping

, event count when the block is Singular under
non-negative flipping
4. Perform comparison between
and
, and
and
,
and estimate the block labels:

.

.

.

.
In our model, the threshold value
is taken as 1.8.
5.

Thus, on the basis of block labels these are classified into 4
distinct groups
and
.

In our proposed model, the remaining blocks (apart from the
four categories as defined above), are excluded from further
processing.
In comparison to the original medical image, the value of
and
blocks usually increases in the stego
images after message embedding. In typical RS steganalysis
model or RS analysis, this resulting feature is detected that
signifies the presence of certain hidden data in the image.
Therefore, to alleviate this issue, in this paper, we intend to
reduce the value of
blocks and to achieve this, we have
applied AGA algorithm that employs the following steps.
1. Chromosome Initialization: Starting from the first pixel
in each block we select each three adjoining pixels as the
initial chromosome.
2. Reproduction and Mutation: In this process, the second
lowest bits in the chromosomes are flipped arbitrarily that
as a result generates numerous 2nd generation
chromosomes .
3. Selection: In selection process, the chromosome having
best fitness is selected to substitute its subsequent initial
chromosome. In our work, we have applied the following
equation (23) to perform fitness estimation.
(23)
where,
refers likelihood of
and
signifies the likelihood of
. The parameters,
presents the weight estimated by experimental process, which is
applied to control the visual quality of the stego image after
message embedding. In addition, it is used to control the weight
for hidden data security provision. For illustration, for a predefined
value, higher value of
and
signifies higher
security provision, which is significant for biomedical data
transmission over insecure or uncertain channel conditions. With
this motivation, in our model we intend to enhance the value of
fitness function, i.e., PSNR.

4. Estimate the adjusted image blocks
and
. The
condition when
, the block is considered as
optimally adjusted.
5. Crossover. To perform crossover, the chromosomes are
shifted by one pixel, which is then followed by Step 2.
Here, it should be noted that based on the fitness value in
our approach, the number of pixels to be shifted does
vary, thus enabling adaptive genetic algorithm (AGA)
implementation.
Once, performing block adjustment, we have calculated the
value of
of the biomedical image under
study and in case the difference between
and
(and/or and
) is more than 5%, the next block is adjusted.
This overall process not only enhances the embedding capacity
but also strengthens the imperceptibility of the stego image.
The results obtained in this study are discussed in the next
section.
V.

RESULTS AND DISCUSSION

This paper presented a novel and robust evolutionary
computing enriched RS attack resilient medical image
steganography (MIS) model to be used for telemedicine
purposes. Unlike traditional approaches, our proposed method
can be considered as multi phase optimizations model to achieve
a robust steganography, where at first to ensure optimal security a
dual security feature has been incorporated. To achieve this at
first a generic RSA cryptosystem was applied over patient’s
details and corresponding diagnosis or doctor information. On
the other hand, to strengthen steganography model a robust
Discrete Ripplet Transform (DRT) technique was derived so as
to perform message embedding in transform domain. Unlike
existing integer wavelet transformation (IWT) techniques, our
proposed DRT method alleviates major singularity problem and
in addition assures edge information preserving, which is of
paramount significance for medical data to enable flawless
diagnosis decision in telemedicine. Thus, applying DRT, the LSB
embedding has been performed so as to hide secret data into
medical image. Further, considering the inevitable need to enrich
imperceptibility and higher embedding capacity, in this research
work an efficient evolutionary computing algorithm named
Adaptive Genetic Algorithm (AGA) based optimal pixel
adjustment process (OPAP) has been developed with intend to
reduce the entropy introduced due to message embedding. Now,
realizing the fact that message embedding in medical image do
often alters the pixel uniformity and hence applying any
statistical relationship between pixel the presence of any secret
image could be traced. RS attack is one of the dominating
statistical analysis approaches to trace hidden secret information
in image. Considering this as a motivation, in this research paper,
we intended to derive a novel RS resilient steganography model,
AGA based block flipping approach has been applied over stego
image (i.e., biomedical image with secret information). The
performance of the overall proposed steganography model is
examined in terms of peak signal to noise ratio (PSNR),

embedding capacity, RS analysis or steganalysis results before
and after AGA based flipping and optimization. To assess the
performance of the proposed model, the investigation has been
made for both the color magnetic resonance image (MRI) images
as well as gray MRI images. Similarly, varying the payload, the
PSNR and histogram variations have been analyzed. Being a
reversible image steganography model, inverse DRT has been
applied at the receiver end to retrieve the embedded message
from the stego image.
The overall model has been developed using MATLAB
2015a software tool, where a well developed Graphical User
Interface (GUI) has been developed (Fig. 3) to feed the data
(medical image or the cover data and patient information). The
developed GUI facilitates user to select input data, and user
defined key series to perform encrypt secret information at the
transmitter end, before embedding it to the cover image. Further
at the receiver to perform extraction, encryption key is used to
retrieve the secret key; however before it Inverset DRT s applied
to decompose stego image and retrieve hidden information from
coefficients. The schematic and overall implementation from is
presented in the following GUI (Fig. 3).

Fig. 3. Graphical User Interface to perform

Some of the data used for the experimental process is given in
Table 1. Here, to assess efficacy of the proposed model both the
color as well as gray biomedical image have been taken into
consideration.
TABLE I.

BIOMEDICAL DATASET

Colour
Biomedical
Image
CBD1

CBD2

CBD3

CBD4

GBD3

GBD4

Gray
Biomedical
Image

GBD1

GBD2

At first the proposed steganography model has been assessed
in terms of PSNR, where to examine the relative efficacy a
parallel model using Integer Wavelet Transform has been
developed. The algorithm has been developed for both the IWT
as well as proposed DRT based message embedding.
TABLE II.

CBD1
CBD2
CBD3
CBD4
GBD1
GBD2
GBD3

IWT
(Original
Cover Image)
26.048
31.615
27.616
31.770
49.242
37.502
40.621

GBD4

40.169

After OPAP

26.615
34.714
28.044
32.168
49.550
38.091
41.142

DRT
(Original
Cover Image)
37.028
41.540
42.017
38.807
52.931
44.893
46.888

40.256

47.001

49.785

GBD3
39.664
44.002
45.133
44.698
55.063
47.017
49.094

As depicted in above table (Table 1), the proposed discrete
Ripplet transform based steganography enables higher PSNR as
compared to the other traditional integer wavelet transformation
based embedding technique. The results also reveal that the use
of AGA based optimal pixel adjustment process strengthens the
process of LSB embedding that as a result not only enhances the
message bit placement uniformly across the cover image, but also
its efficient extraction. Since, in our work, we have incorporated
AGA for OPAP in both the cases, i.e., with IWT based
embedding as well as DRT based patient information embedding;
however the better performance by DRT based approach reveals
that the image decomposition and information hiding at the
coefficients is better with DRT technique. This is the fact that
PSNR is greatly influenced at the receiver terminal where the
medical image is extracted or decomposed to retrieve the hidden
information, the consideration of pixel uniformity and sequential
information retrieval is better with DRT technique that as a result
enables better message retrieval and hence higher PSNR. In
addition, the results also state that block based data hiding is
better and computationally efficient than the pixel replacement
based approaches. It should be noted that in our proposed DRT
based steganography, block based data embedding has been
performed,
TABLE III.

EMBEDDING CAPACITY

PSNR (After OPAP)
Payload
(bits)

CBD1

CBD2

CBD3

4412
6096
6883
4412
6096
6883
4412
6096

GBD1

GBD2

PSNR ANALYSIS

After
OPAP

CBD4

IWT based
embedding

DRT based
embedding

DRT based
steganography
Embedding
Capacity (%)

26.615
26.010
26.091
34.714
33.071
33.004
28.044
28.019

39.664
39.900
38.997
44.715
44.610
44.511
45.133
45.280

42.20
47.10
41.00
42.00
44.01
45.17
52.03
52.96

GBD4

6883
4412
6096
6883
4412
6096
6883
4412
6096
6883
4412
6096
6883
4412
6096
6883

28.003
32.168
32.110
31.104
49.550
48.021
46.019
38.091
37.914
36.198
41.142
40.018
37.113
40.256
37.071
33.825

45.100
44.698
44.550
44.512
55.063
55.008
54.890
47.017
45.012
45.007
49.094
49.047
48.530
49.785
49.413
49.002

53.49
33.18
34.32
34.63
42.01
42.65
42.93
33.18
35.01
36.67
44.00
45.01
47.03
37.97
45.41
49.03

Thus, observing above mentioned results (Table 2), it can be
found that the proposed DRT based steganography outperforms
IWT based approach in terms of higher embedding capacity as
well as PSNR, even for higher payload. One more interesting
outcome has been observed that when varying (precisely
increasing) payload, the PSNR decreases in case of IWT based
steganography. On the contrary, with DRT the decrease in PSNR
is very small and can be percept as having negligible or very
small affect on imperceptibility. The results affirm that the
proposed steganography model performs uniform even varying
payload without any significant PSNR degradation. Considering
the effectiveness of the proposed DRT and AGA based
steganography model, we have further enriched it with RS
resiliency ability, where AGA has been applied to perform block
flipping and block adjustment. Here, we considered the
hypothesis that block adjustment can perform better than the
standard pixel adjustment based OPAP. The following results
present the RS analysis outcomes from the proposed
steganography model.
In our model, we have applied Stegnalysis on all eight
biomedical images. The results obtained are given In Table 3.
Here, it can be found that the value of
and
increases as per increase in embedding capacity. It signifies the
rise in the complexity to adjust the blocks so as to preserve the
imperceptibility.
TABLE IV.
Data
CBD1
CBD2
CBD3
CBD4
GBD1
GBD2
GBD3
GBD4

RS ANALYSIS (COVER IMAGE)

0.0093
0.0052
0.0010
0.0026
0.0003
0.0011
0.0004
0.0044

0.0082
0.0018
0.0011
0.0047
0.0043
0.0003
0.0013
0.0098

On the other hand, Table 5 presents the differences (
and
), where it can be found that the value of
decreases by using AGA based block mapping and block
adjustment process that makes it difficult or even infeasible for
attackers to identify or detect the secret information hidden in the
cover image (biomedical image).

TABLE V.
CBD1
CBD2
CBD3
CBD4
GBD1
GBD2
GBD3
GBD4

RS ANALYSIS ( STEGO IMAGE)

0.0078
0.0024
0.0020
0.0031
0.0097
0.0052
0.0097
0.0043

0.0006
0.0058
0.0047
0.0013
0.0044
0.0089
0.0084
0.0022

The histogram results of the original and AGA-DRT based
stego-image are presented in Table 6.
TABLE VI.

GB
D1

GB
D2

HISTOGRAM FOR ORIGINAL AND STEGO-IMAGES
Histogram (Cover image-Stego-image)

CB
D1

GB
D3

CB
D2
GB
D4

CB
D3

Thus, the overall results affirm that the proposed
steganography approach achieves optimal balance between the
biomedical image security, embedding capacity as well as image
quality that can play significant role in telemedicine applications.
VI.
CB
D4

CONCLUSION

This paper presented a novel and robust steganography
technique for medical image steganography to be used for
telemedicine application. Considering the inherent medical image
characteristics and critical associated information, a discrete
Ripplet transform (DRT) based message embedding model was
developed, where patient’s details were embedded in transform
domain. Unlike, wavelet transform technique the ability of DRT
to deal with singularity issues and image decomposition
strengthened the proposed work to enable efficient LSB message
embedding, which eventually led higher imperceptibility and
embedding capacity, while maintaining better image quality. To
further strengthen the imperceptibility and embedding capacity,

adaptive genetic algorithm based optimal pixel adjustment
process was performed. In addition, to alleviate the issue of
statistical attack such as RS attack in channel, AGA based block
flipping and adjustment model was developed, where the
proposed system has exhibited better outcome and RS resiliency.
This paper incorporated dual model security feature by applying
RSA based secret data encryption and DRT and AGA enriched
steganography that as a combined model has exhibited better
PSNR, embedding capacity, imperceptibility and attack
resiliency. These all novelties support it to be used for real time
telemedicine purposes. In future, other type of Ripplet transforms
such as RT Type II and RT Type-III can be explored to have
better efficiency.

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