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http://www.iaeme.com/IJMET/index.asp 784 [email protected]
International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 10, October 2018, pp. 784–798, Article ID: IJMET_09_10_082
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=9&IType=10
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
STEGANO-CRYPTOGRAPHY FOR SECURED
TRANSMISSION OF MEDICAL X-RAY
IMAGESUSING CHAOTIC MAPS
V.Praneeth Kumar Reddy, Annis Fathima A
School of Electronics Engineering, VIT University, Chennai
ABSTARCT
Recently, many fraudulent attacks towards life/health insurances by providing fake
health information are in increase. A secured transmisssion of the medical images to
avoid misrepresentation of medical report by hacking the medical image sent by the
medical practitioner to the insurance provider. In this paper, steganography and
encryption techniques are combined to protect the patient confidentiality, and increase
the security in medical images. In existing approaches, steganography or water marking
are used to secure the medical images, but these techniques cannot assure robust
security on transmission. Hence, a combined steganography to incorporate the patient’s
details and advanced encryption techniques to secure the transmission is required. An
approach for encryption and steganography using discrete non-linear system that
provides least correlation for secured transmission is proposed. Henon and Chebyshev
maps are the discrete non-linear systems adopted in the process. A database of 100 X-
ray images is considered for performance evaluation. The metrics such as entropy and
correlation coefficient for encryption and metrics such as PSNR, MSE, and elapsed time
for steganography are evaluated. It is observed that the combined Henon and
Chebyshev map gives better results for encryption and the proposed steganography
gives better results than LSB approach. The combination of steganography and
encryption will give robust security for the transmission.
Keywords: Chaotic map, Encryption, Steganography, Medical X-ray images
Cite this Article V.Praneeth Kumar Reddy and Annis Fathima A, Stegano-
Cryptography for Secured Transmission of Medical X-ray Images using Chaotic Maps..,
International Journal of Mechanical Engineering and Technology, 9(10), 2018, pp. 784–
798.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=9&IType=10
STEGANO-CRYPTOGRAPHY FOR SECURED TRANSMISSION OF MEDICAL X-RAY
IMAGES USING CHAOTIC MAPS
http://www.iaeme.com/IJMET/index.asp 785 [email protected]
1. INTRODUCTION
In recent times, fraud and abuse are extensive and expensive in any health care systems. For
illegal medical insurance claim, procedures like unbundling of claims, double billing,
misrepresentation etc. are done. Fraudulent submission of medical images will result in
misrepresentation deliberated to result for the uncertified benefits and false claims to get
undeserved payments [23, 24]. Illegal data attacks are wide spread in communication networks
and retaining the medical x-ray images is predominant for the precise claim. To avoid
misrepresentation of the medical images, the patient’s details can be integrated in the medical
image. To avoid any further attack, secured transmission has to be ensured.
Nowadays, there is a move to change from paper-based medical record to keep digital-based
record. In medical centres, the medical images are obtained by radiographer and stored in
Health care information system. From the information system, only the medical practitioners
will have the access to retrieve the images stored for diagonising. It needs to be transmitted
when expert opinion is required. It needs to be transmitted also in the case of insurance claim.
While on internet, the image is exposed to threat. The data can be modified by the patient or
some hacker can use the information to have false insurance claims. The modified image can
also be used as evidence for pressing false charges against medical centers. To answer the stated
concerns, the authentication of images is of highest priority.
Steganography and digital watermarking are primarily used for the security in medical
images, which will hide the patient details in the medical image, yet additionally there is a need
for security in the medical images. Hence, in addition, encryption of the medical image is
proposed. In this paper, the encrypting module is of concern and discrete non-linear systems
are used to encrypt the image.
In the traditional approach steganography techniques rely on using LSB algorithmic
technique. In this method, the message is converted to binary bits and later inserted into LSB
of the pixel values of the image. This approach is very common and hence easier to hack as
there is no key. In later stages, algorithms utilize pseudorandom generator to identify the
random pixel location and LSB is replaced with message bit [1]. The security aspects in it are,
random generator solely depends on seed value and hence key are relatively less secured. In
place of random generator, chaotic maps are conventional to generate the random numbers,
which have comparatively more key values to come up with random numbers and subsequently
also increases the security.
The widely used image encryption techniques are AES, DES and RSA. Advance Encryption
Standard (AES) algorithm which is first proposed by Danmen and Rijmen, is an iterated block
cipher. But the traditional AES algorithm have some disadvantages like singleness of the key,
deficiency of key space. These will cause the security problems. Data Encryption Standard
(DES) is a symmetric key encryption algorithm. In these algorithms, permutation process will
take more time and chances of mistakes are also high. It is computationally expensive process
and there is a chance of erroneous key, as the key length is 256 bits.
The existing AES, DES and RSA techniques exhibit low security and weak anti-attack
ability. To resist the mentioned problems and the security attacks; chaotic maps are used for
encryption and steganography of the medical X-ray images [2]. Chaotic systems improve the
security and robustness and it forms the base for efficient encryption system. Discrete non-
linear systems are used to generate sequences which are used for the encryption. In this paper,
Henon map and Chebyshev map are used for the encryption of the medical images. First, the
X-ray medical image is encrypted with 2D Henon map followed by 2D Chebyshev map. The
security analysis of the algorithm is ensured by evaluating the parameters like key sensitivity,
correlation coefficient, entropy for encryption and PSNR, MSE and elapsed times for
V.Praneeth Kumar Reddy and Annis Fathima A
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steganography. For performance evaluation, database of 10 medical x-ray images are
considered.
2. RELATED WORK
The literature survey is of two parts. The first part is on the steganography how the details are
incorporated in the cover image and the steganalysis. The second part is on the encryption
techniques used in the medical images and security analysis on those techniques.
A fast approach for the image encryption [3] is proposed by Reza Moradi Rad, et.al (2013)
with scan patterns and XOR function for simple images. Scan pattern is a spatial accessing
technique to generate a means to select location. Initially blocks are rearranged and then pixels
in each block are rearranged using scan patterns and all blocks are XORed with arbitrary blocks.
This approach is fast and modest.
Zhang et.al (2009) has reviewed the usage of chaotic maps, DES and also the combination
of both for image encryption algorithm [4]. The algorithms were simulated and based on the
analysis proposed an algorithm utilizing logistic Chaotic map. Primarily the scheme generates
pseudo-random sequence from the logistic chaos sequencer for rearranging RGB of the image
chaotically. Double time encryption is done using the improved DES. It has high sensitivity and
the neighboring RGB relevance values are nearly zero.
Symmetrical hybrid based 128 bit key AES-DES algorithm for motion image transmission
[5] was proposed by Vishnu, et.al. (2008). It works on the idea of integrating the AES within
the Feistel network of DES for image encryption. The performance of Hybrid AES-DES
algorithm was better compared to current AES algorithm.
A scheme of AES where the independent round key was produced by the two dimensional
chaotic maps [6] was proposed by Jianhua and Hui (2013). With the mathematical model,
hacker can obtain cipher key by attacking one round of round key. So the generated key is
proposed to change after every round. In this algorithm, the cipher key is directly filled into
the head of round key, so cipher key can be obtained by exhaustive method when the
information of key is leaked in a certain degree. It improves the defect of the traditional AES
algorithm.
A fast and high security image encryption algorithm [7] is proposed by Salim and
Nasharuddin (2014), this approach uses BCD code based decomposition, reordering and a
simple scrambling process to shuffle binary bit planes. A shift column is applied to image which
is constructed after scrambling the bit planes to advance security. In this algorithm a new
scrambling operation was also introduced and applied on bit planes to alter the pixel values and
increasing the key space by 12!.
A fractal based image encryption system [8] is proposed by Kamla (2013), the method has
been adopted using single and multi-fractals. Encryption is based on confusion and diffusion
processes and image information is concealed in complex details of fractal images. For a
general encryption system utilizing multiple fractal images is presented to improve the
performance and to increase the key to hundreds of bits, achieved through the parameters like
multiplexing, feedback delay and independent shifts.
Jolfaei and Wu (2016) reexamined the previous works on cryptanalysis [9] of different
image encryption systems. Chandra, et.al (2015) has analysed chaotic based image encryption
techniques [10] and also reviewed the related works for each technique.
Zhang, et.al (2005) projected and enhanced the properties of confusion and diffusion in
terms of discrete exponential chaotic maps [11], and designed a key scheme to resist the static
attack, differential attack and grey code attack. Image encryption algorithm using Arnold or
STEGANO-CRYPTOGRAPHY FOR SECURED TRANSMISSION OF MEDICAL X-RAY
IMAGES USING CHAOTIC MAPS
http://www.iaeme.com/IJMET/index.asp 787 [email protected]
Baker and Henon chaotic system is proposed by Abu Zaid et.al (2013). The process also utilizes
code books and Ciphering Feed Back for encryption [12].
Optical image encryption is proposed by Ahmed M.Elshamy (2013), based on chaotic baker
map and double random phase encoding (DRPE) [13]. Chaotic baker map is a permutation
based tool which randomizes the pixel positions based on a secret key in a square matrix.
Algorithm involves two stages to increase security, stage one is pre-processing stage in which
chaotic baker map is performed on the original image, stage two involves classical DRPE.
Singh and Kaur (2011) compared four chaotic maps and noise effects on an encrypted image
[14]. The image encryption is performed using chaotic maps and DNA. In the images with eight
bit grey level each and every pixel is indicated by the DNA sequence of length four. The result
shows that cross chaotic map gives better result but at the cost of increasing complexity.
Authentication system for medical images using steganography [15] is proposed by
Hisham, et.al (2014). Ahmad, et.al (2014) proposed method to protect the medical data using
shared secret mechanism and steganography for two and one bit LSB [16].
For image steganography, the very common approach utilised is LSB algorithm. An
improvement is made in the basic LSB based steganography [17] by Akthar, et.al (2014). Here
the bit inversion technique is organized to improve the quality of the stego-image. LSBs of
pixels or cover image are inverted only for the specified pattern so that less number of pixels
will be modified between cover image and stego-image as compared to the basic LSB
algorithm.
A revisited LSB approach is proposed by Jarno Mielikainen (2006), in this method choice
of adding bits into cover image is random [18]. The embedding process is done using a pair of
pixels as a single unit, where each pixel will carry one bit of information. The proposed method
will embed the data with only few changes in cover image.
LSB algorithm with the combination of midpoint circle approach to choose the pixel
position for hiding the message [19] is proposed by Verma, et.al (2014). With the dimensions
of the cover image found the center of the image to be used to choose the pixels that would be
used to hide the message.
Image steganography based on the DES by means of S-Box mapping and a secret key [20]
is proposed by Manoj, et.al (2012). It offers high security as extraction is impossible without
the awareness of secret key and mapping rules of the algorithm. But, this scheme not just
scrambles the data but also modifies the intensity of pixels which is not favorable.
Hisham, et.al (2014) authenticated medical images using Hilbert numbering [15]. The
authors performed watermarking using twisting style numbering which are good for embedding
but in square shape only. Later enhanced to Hilbert numbering which is appropriate to all
shapes. The capacity of the proposed algorithm is high, it will embed the data all over the image,
regardless region of interest and region of non interest.
A security technique for medical images in health systems [21] is proposed by Kester, et.al
(2015) through encryption and authentication process. The algorithm is fully recoverable
encrypted and watermarked image processing, initially authentication of the medical images
then proceeding for encryption. The entropy and mean values of the images were computed,
and it was found to be same for all ciphered and plain images.
Dagar, 2014 mentioned that, utilising an approach with two different keys to hide the
message bits at random pixels of the cover image [22] is more secured. From literature it is
learnt that the chaotic maps gives the efficient encryption results and using cross chaotic maps
will give enhanced results but increases the computational complexity. For the medical images
robustness is of more importance, hence chaotic maps are used and the complexity factor is not
V.Praneeth Kumar Reddy and Annis Fathima A
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considered in the work for securing the images. Similarly, for the steganography chaotic maps
are used to improve security by locating and hiding the information.
3. DISCRETE LINEAR SYSTEMS Discrete non-linear systems are complex in structure and dynamic in nature. Random numbers are generated with
the initial values and are very sensitive to the initial conditions. This is similar to butterfly effect. Butterfly effect
solely depends on the initial conditions. A small uncertainty in initial conditions may result in large difference in
the resulting map. It is unendurable to predict the succeeding reaction. Hence this sensitivity is very convenient in
the security justification.
3.1 Henon Map
Henon map was first proposed in 1976 by French astronomer Henon. Henon map is discrete-
time and dynamical system. Based on the initial values and arbitrary constants the random
sequence is generated. The Henon map is generated using the Eq. 1 and 2
Xn+1 = - a Xn2 + b Yn +1 (1)
Yn+1 = Xn (2)
In these expressions, a and b are arbitrary constants; and X0 and Y0 are initial values. The X
and Y variables will form the Henon map, H. These four values form the initial keys for the
Henon map. Henon map with a=1.4, b=0.3, X0=0.1 and Y0=0.1 is shown in the Figure 1(a). The
initial values are the main asset for the generation of the sequences. As like butterfly effect, the
map generate sequence of numbers with high sensitivity towards initial and arbitrary values.
3.2 Chebyshev map
Chebyshev map is introduced by Chebyshev. Chebyshev map has more randomness than Henon
map as shown in the Figure 1(b). The random sequence is generated using the Eq. 3 and 4.
Un+1 = Cos(c arc CosUn) (3)
Vn+1 = Cos(d arc CosVn) (4)
U0 and V0 are the initial values, c and d are the arbitrary constants. The initial values later
acts as key for the logical operation. Figure 1(b) is the chaotic Chebyshev map, C, constructed
with the initial values c=1.4, d=0.3, U0=0.1 and V0=0.1.
(a) (b)
Figure 1 (a) Henon map (b) Chebyshev map
STEGANO-CRYPTOGRAPHY FOR SECURED TRANSMISSION OF MEDICAL X-RAY
IMAGES USING CHAOTIC MAPS
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4. PROPOSED APPRAOCH FOR SECURING IMAGES
The 2D chaotic maps namely Henon and Chebyshev are used in the proposed work i.e., for
Steganography Chebyshev map is used and for the efficient Encryption both
Figure 2 Block diagram for generating Crypto-Stego image
Henon map and Chebyshev map are used consequently. To increase the randomness and
robustness, both the maps are used consecutively.The objective of the steganography is to
incorporate the patient’s details in the medical image. In the proposed algorithm for
steganography Chebyshev chaotic map is employed for the higher security in steganography
process.
The flow diagram for obtaining the stego-crypto image is shown in Figure 2. In the
transmitting side, the message is embedded in the medical image. Figures 4 and 5 gives block
diagrams for embedding and retrieving the patient details. The medical image, patient’s details
and the key for generating the chaotic map are inputs in the transmitting side. Initially the
message to be embedded is converted using ASCII code. In the information of length N, initially
twenty one bits are allotted to provide the information about message length as given in Eq. 5
where N gives the total number of bits of information to be inserted. The initial twenty one bits
specify the number of random values to be generated for retrieving message. For selection of
the location, Chebyshev map is utilized and the information bits of the character are embedded
in the LSB of the selected pixel location.
N=21bits + message bits (5)
From the initial values, random numbers are generated by the chaotic map, these initial
values act as keys for the steganography. The generated random numbers are normalized and
standardized to fall in the range {1, mn}, where mxn is the size of the cover image.
V.Praneeth Kumar Reddy and Annis Fathima A
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Figure 3 Block diagram for steganography
The generated values, XN aid in identifying the pixel location iN, jN in the cover image using
the Equations 6 and 7. iN, jN gives the row and column position in the image.
iN = floor (XN/m) +1 (6)
jN = XN mod(m) (7)
The initial 21 bits for message length and the message bits are inserted in the location iN,
jN. The resultant stego image is safer as a result of the additional sensitivity of the keys for the
generation of random numbers. The algorithm for embedding is given below.
Algorithm for embedding process
Input: Cover image, Stego key, Message
Output: Stego Image
{Step1: Read the cover image.
Step2: Read the message.
Convert message to ASCII
Convert ASCII to binary
Step3: Read the key for chaotic map.
Step4: Generate chaotic map using Stego keys
Get the values, XN, in range {1, mn}.
Step5: Read the normalized values.
for N values
{
Find the pixel location using Equation 5 and 6.
Replace LSBs in cover image with message bit.
}
Step7: Write stego image.
}
STEGANO-CRYPTOGRAPHY FOR SECURED TRANSMISSION OF MEDICAL X-RAY
IMAGES USING CHAOTIC MAPS
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At the receiver end the embedded information is retrieved from stego-image using chaotic
map keys as given in Figure 3. With the keys of Chebyshev map, initial 21 random numbers
are generated. These 21 bits give the information of the no. of message bits i.e. N-21. Then N-
21 random numbers are generated in the similar way. From the LSB of the pixel locations
obtained using Eq. 6 and 7, the message bits are derived. The 7 bits are grouped to form ASCII
code, and then converted to character. This finally gives the message and the cover image is
retrieved.
Figure 4 Block diagram for Encryption process
Figure 4 gives the steps involved in the process of the Encryption. With the initial keys,
chaotic maps are generated. The initial values and arbitrary constants act as the keys for the
encryption. Both the Henon and Chebyshev map has independent four keys making it to 8 keys
on total. With the initial keys mentioned in the previous section, random discrete values are
generated. The original image, I, is first encrypted with the generated Henon map, H, as given
in Eq. 8 giving the subsequent encrypted image, I1. Then the image is further encrypted using
Chebyshev map, C as given in Eq. 9. The final resultant image I2 is more secure as it has wide
randomness and very sensitive key.
(8)
(9)
On the other end i.e. in the receiving side, the image I2 is first decrypted with Chebyshev
map, C and later with Henon map, H, to get back the original image I.
5. RESULTS AND DISCUSSIONS
In this work of securing the medical images, 100 medical X-ray images of chest, fractured
shoulder and ankle images randomly collected from the web are used. The implementation is
done using Matlab2013a.
5.1 Performance Analysis for Steganography
The information to be embedded in the medical X-ray image, includes the patient details
and the number of binary bits required to encode the details in ASCII. As the no. of bits required
to write the patient details vary from person to person, it becomes essential to mention the
message length. Otherwise the more random numbers will be generated leading to the
∑ ∑−
=
−
=⊕=
1
0
1
01 ),(),(),(m
i
n
jjiHjiIjiI
∑ ∑−
=
−
=⊕=
1
0
1
0 12 ),(),(),(m
i
n
jjiCjiIjiI
V.Praneeth Kumar Reddy and Annis Fathima A
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unnecessary computation or it may also lead to difficulty in decoding the required information.
The message length is given in first 21 bits in ASCII format. The format in which the details
are encoded is given below:
{Message length (21 bits); Name; Age; Gender; Date; Clinical place; Major indication}
Each character in the message are converted to ASCII and then to 7 bit binary code. At the
receiver end excluding first 21 bits each 7 bit binary code gives one character. Figures 5(a) and
(b) gives the cover image and stego-image constructed using the Chebyshev map. Henon map
is also used for steganography but due to more randomness of Chebyshev map then Henon map,
it is more preferred than Henon map.
To evaluate the performance characteristics of proposed algorithm, took measures such as
PSNR (Peak Signal to Noise Ratio), Correlation coefficient and Evaluation time. The result is
compared with the existing Steganography approach using LSB algorithms.
5.1.1 Peak Signal to Noise Ratio
Peak Signal to Noise Ratio (PSNR) gives the noise content in an altered image compared to
source image, and in this case it is between cover image and stego image. MSE is Mean Square
Error; it gives cumulative square error between cover image and stego image. MSE and PSNR
between two images are calculated using the Eq. 10 and 11, where m and n are number of rows
and columns of an image, L is the maximum graylevel value i.e. 255.
Table 1 tabulates the evaluated PSNR value for stego-image computed using existing LSB
algorithm and the proposed approach using Chebyshev map.
(a) (b)
Figure 5(a). Cover Image and (b) Stego Image
( ) ( )[ ]
nm
jiIjiI
MSE
m
i
n
j
∗
−
=
∑∑−
=
−
=
1
0
1
0
2
21 ,,
(10)
=
MSE
LPSNR
2
10log10
(11)
TABLE 1. Comparisons of PSNR for stego-image
S.No. PSNR for LSB
algorithm(dB)
PSNR for
proposed
approach(dB)
Image 1 71.5485 77.5881
Image 2 75.2996 79.9217
Image 3 80.5193 86.4833
Image 4 71.4873 78.0099
Image 5 77.2018 82.8068
Image 6 72.9307 77.6082
Image 7 71.4592 76.4243
STEGANO-CRYPTOGRAPHY FOR SECURED TRANSMISSION OF MEDICAL X-RAY
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Image 8 82.6137 86.8169
Image 9 83.7337 89.0713
Image 10 78.7279 83.9378
5.1.2 Correlation Coefficients
Correlation coefficient is the measure of similarity between two images. The correlation
coefficient between the cover image and stego-image are very close. The results are tabulated
in Table 2 with comparison to the traditional algorithm.
TABLE 2. Comparison of correlation coefficients for stego-image
S.No.
Correlation
for LSB
algorithm
Correlation for
proposed approach
Image 1 0.999998247 0.999999471
Image 2 0.999997055 0.999999891
Image 3 0.999998980 0.999999971
Image 4 0.999998552 0.999999567
Image 5 0.999994691 0.999999830
Image 6 0.999993655 0.999999797
Image 7 0.999991853 0.999999758
Image 8 0.999993771 0.999999801
Image 9 0.999999320 0.999999790
Image 10 0.999999151 0.999999456
5.1.3Elapsed Time
Elapsed time is the time taken to obtain the stego-image using the proposed algorithm. Table 3
gives the result for elapsed time. From the table it is observed that elapsed time for the proposed
algorithm is lower than the simple LSB algorithm. In LSB algorithm initially every LSB of
pixels are made to zero and then message bits are inserted one by one in an order, but in the
proposed algorithm only at the selected random pixel locations message bits are inserted at
LSB. This results in the time conservation.
5.2 Performance analysis for Encryption
For the better encryption procedure should be robust to security attacks and statistical attacks.
To evaluate the
TABLE 3. Elapsed times in computing stego-image
S.No.
Time taken for
LSB
approach(sec)
Time taken for
proposed
approach(sec)
Image 1 0.6239 0.5579
Image 2 0.8480 0.7339
Image 3 1.2943 0.8319
Image 4 0.6426 0.6170
Image 5 0.7848 0.5529
Image 6 0.5614 0.5676
Image 7 0.5704 0.5327
Image 8 1.2212 0.6889
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Image 9 1.4514 0.6730
Image 10 1.0211 0.5295
performance of the proposed algorithm, the measures such as key sensitivity, entropy and
correlation coefficient are measured.
For the comparative analysis, the encryption was done with Henon map and Chebyshev
map individually. Then it is compared with the approach combining both the maps.
5.2.1 Histogram Analysis
For an image, histogram plot gives the frequency of the intensity levels in an image. From
histogram of an image it is easy to estimate the intensity or tonal distribution Figure 6(a) shows
the histogram of the original image. Figure 6(b) shows the histogram of the encrypted image
using Henon map.
On comparison, it is clear that the encrypted image is of high contrast when compared with
that of the original image. Similarly Figure 6(c) shows the histograms of the encrypted images
using Chebyshev map. For the image encrypted with both maps, histogram is shown in Figure
6(d). From the results histogram for original image and encrypted image is different. Due to
high contrast it will be difficult to extract the information without keys. Histogram for the
original image and decrypted image is observed to be same.
The encryption of an image is initiated with the initial keys and they are required to be more
sensitive. The sensitivity of the keys is directly related to the robustness that can be achieved.
In case of Henon Map the initial key values are a=1.4, b=0.3, X0=0.1 and Y0=0.1.
(a) (b)
(c) (d)
Figure 6 Histogram of (a) Original Image (b) Encrypted with Henon map (c) Encrypted with
Chebyshev map (d) Decrypted with different keys
5.2.2 Key Sensitivity
The original and the encrypted image with H values are shown in Figure 7(a) and 7(b)
respectively. Figure 7(c) shows the decryption of the encrypted image with the same key values
except for very minor change in the value of A as X0=0.001. From the resultant image shown it
is clear that the keys are very sensitive.
Similarly, for the case of encryption with Chebyshev map. The initial keys are c=1.4, d=0.3,
U0=0.1 and V0=0.1 encrypted with these values and to get the exact image after decryption its
mandatory to use the same keys. For the analysis purpose, the initial key values are modified
as c=1.3 and d=0.33, and the result is shown in Figure 7 (e).
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(a) (b) (c) (d)
(e) (f) (g)
Figure 7 (a) Original Image (b) Encrypted Image using only Henon map (c) Decrypted using
different key in Henon map case (d) Encrypted Image using only Chebyshev map (e) Decrypted Image
Using different key in Chebyshev map (f) Encrypted Image using Henon Map and Chebyshev Map
(g) Decrypted Image with original key
On using the combination of two maps the keys are more sensitive and altering even one
key will not extract the original image after decryption. On subjective analysis, from the results
it is proven that Henon map have more sensitivity and the large key space, better key sensitivity
and robust security which is more particular in security of medical images. As it has large key
space it will be difficult to debug the key by search method.
Figure 7(f) shows the encrypted original image using the combined chaotic maps. The
decrypted image on the receiving side using 8 keys is shown in Figure 7(g).
5.2.3 Correlation and Entropy
The other performance metrics used for evaluation are correlation coefficient and entropy.
Correlation defines the relation or matching between two images, if correlation value is 0 then
there is a random relation or non-linear relation between two images and if the value is 1 then
there is linear relation between two images. For the better encryption the correlation coefficient
should be 0. Entropy is the measurement of uncertainty related with the random variable.
The results of entropy for the sample 10 medical x-ray images is given in Table 4. Table 5
gives the correlation coefficient between original and encrypted images
TABLE 4. Entropy of sample images
S.No.
Origin
al
Image
Encr
ypted
with
Heno
n
Encryp
ted
with
Chebys
hev
Encryp
ted
combo
map
Image 1 7.4771 0.0379 0.0370 0.0357
Image 2 7.5451 0.0352 0.0398 0.0409
Image 3 7.8379 0.0359 0.0284 0.0355
Image 4 7.7466 0.0391 0.0291 0.0368
Image 5 7.8612 0.0359 0.0289 0.0367
Image 6 7.3164 0.0363 0.0387 0.0365
V.Praneeth Kumar Reddy and Annis Fathima A
http://www.iaeme.com/IJMET/index.asp 796 [email protected]
Image 7 7.3976 0.0380 0.0453 0.0367
Image 8 7.7988 0.0340 0.0352 0.0345
Image 9 7.7367 0.0344 0.0344 0.0364
Image10 7.3454 0.0387 0.0415 0.0357
The correlation coefficient is calculated between the original image and encrypted image,
also the correlation between original image and decrypted image are evaluated. The results are
tabulated in Table 5. The value of correlation coefficient on an average of 0.025 when image is
encrypted using Henon map, the value on an average of 0.035 when encrypted with Chebyshev
map and the average value is 0.002 when encrypted with the combination of two maps. Hence
correlation coefficient is very near to zero when used the combination of two maps. The
correlation coefficient between original image and decrypted image is found to be 1.0.
TABLE 5. Correlation coefficients
S.No.
Encry
pted
with
Henon
Encrypt
ed with
Chebysh
ev
Encrypte
d with
Henon
and
Chebysh
ev
Dec
rypt
ed
ima
ge
Image1 0.028
5 0.0370 0.0004 1
Image2 0.016
5 0.0398 0.0043 1
Image
3
0.033
0 0.0284 0.0021 1
Image
4
0.033
8 0.0291 0.0023 1
Image
5
0.044
6 0.0289 0.0021 1
Image
6
0.016
8
0.0387 0.0014 1
Image
7
0.020
9
0.0453 0.0004 1
Image
8
0.030
4
0.0352 0.0006 1
Image
9
0.038
5
0.0344 0.0023 1
Image1
0 0.019
1
0.0415 0.0021 1
6. CONCLUSION
A stego-crypto methodology is proposed to avoid misrepresentation of medical X-ray images
for the fraudulent claims. In steganography it is proposed to enclose the details of patient in the
image with the reference of pixel location by random numbers generated using Chebyshev map.
The performance is evaluated in terms of PSNR, elapsed time and correlation. From the
performance characteristics the proposed algorithm gives better results and better security.
Followed by steganography, encryption is done using the chaotic systems. For comparison,
encryption is performed with three approaches, using Henon map, Chebyshev map and
combination of both maps. The performance metrics used for evaluation are histogram, entropy
and correlation. From the results, it is observed that the combination of Henon map and
STEGANO-CRYPTOGRAPHY FOR SECURED TRANSMISSION OF MEDICAL X-RAY
IMAGES USING CHAOTIC MAPS
http://www.iaeme.com/IJMET/index.asp 797 [email protected]
Chebyshev map will give the better results and give robust security to the medical images but
at the cost of computational complexity. As robustness is more specific for insurance claims,
the combined approach is preferred.
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