Prema digital medical image. The decryption is inverse

Prema T.
Akkasaligar and Sumangala Biradar

BLDEA’S
V.P. Dr. P. G. H. College of Engineering and Technology, India

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Abstract

Nowadays
due to rapid growth in the medical field, transmission of digital medical
images over internet has become frequent. Due to lack of security levels in
internet intruders can access the digital medical images and alter the digital
medical images.  Ensuring the security of
digital medical image has become an important issue. In this regard several
image encryption techniques based on deoxyribonucleic acid (DNA) computation
and chao’s theory is already available. But these methods take more
computational time and also digital medical image require high-level security.
To enhance and provide high-level security for digital medical image, multilevel
security is proposed using Lorenz chaotic map and DNA sequences. Also the
selective digital medical image encryption algorithm is proposed to reduce
computational time. In the proposed method, firstly the input digital medical
image is divided into blocks. The selected pixels in blocks are renovated into
DNA encoded matrix. Later, chaotic sequence generated by Lorenz chaotic map is
used to scramble the selected pixels positions of DNA encoded matrix. Finally
DNA add operation is used for the fusion of scrambled DNA encoded matrix blocks
to get an encrypted digital medical image. The decryption is inverse process of
encryption and DNA sub operations in place of DNA add operation. The experimental
result shows that proposed algorithm not only enhances security level but also
reduces computational time. And also encumber exhaustive and statistical
attacks.

Keywords:
Digital Medical Image, Encryption, Decryption, Lorenz chaotic map, DNA
sequences

INTRODUCTION

We
are living in the automation world where everything will be digitalized. In
medical field e-health, smart health and telemedicine are automated systems. These
systems utilize advanced digital
communication system to transfer and receive medical information for end-to-end
communication. These digitalization reduce time and remote centric. However,
due to the open source, the transmission of digital medical image can easily
intercepted by hackers. Hence image encryption technology has a prime concern
in present era. In image encryption the digital image is encrypted then it
sends via insecure channel. At receiver end only authorized user can access the
original image. In this field several cryptographic techniques are available
among those chaotic system and DNA sequence based techniques are advanced
techniques.

 

The chaotic system generates a nonlinear deterministic
pseudo random sequence, which are highly sensitive to initial conditions. It
also has many characteristics, such as, ergodicity, mixing property and
structural complexity. The Lorenz chaos system is high dimensional chaotic map
and it is very complex. Due to these properties, Lorenz chaotic system is used
in the field of cryptography for digital medical image encryption. But because
of limited key space it can easily attacked by the key space and key sensitive
analysis test. Hence new technique called DNA technique evolved in
cryptography.

 

The DNA cryptography is a fresh domain in
cryptography. DNA is used as an information carrier in image encryption. DNA
computing uses biological DNA molecules as computing medium and biochemical
reaction as computing tools 1. The main security is based on DNA structure. The
implementation of DNA computation on image is not easy because of complex DNA
structure. Hence it is used only to encrypt the character information.  For image encryption only DNA sequence is
used as security which is not enough. To overcome from the above drawbacks, the
combination of Lorenz chaos system with DNA sequence is used for image
encryption.”

 

Background

A slight work has been
done by DNA cryptography and chaotic system to transmit a digital medical image
in a secured way. Several encryptions of images had been put forward by using cryptographic
and watermarking techniques.

 

In 2, authors have
proposed cosine number transform (CNT) method for medical image encryption. It
is very sensitive to changes, which is suitable for medical image. The image is
divided into blocks and CNT method is applied twice to encrypt the image. In 3,
authors have presented 2D chaotic map to permute the pixel of the medical
image. The image is divide into blocks, sensitive part of the blocks are
identified and masked with synthetic image to encrypt the selective part of the
medical image. In 4, authors have proposed digital watermarking method. The
entropy and mean of the medical image is calculated to obtain cipher image. The
cipher image is hidden using digital watermarking algorithm to get encrypted
medical image. In 5, authors have proposed the Rivest, Adi Shamir and Leonard
Adleman (RSA) algorithm for encryption and decryption of magnetic resonance
imaging (MRI) images. Further to extort the tumor details K-means, watershed segmentation
is used. In 6, authors proposed least significant bit (LSB) embedding
algorithm   to embed patient privacy
information in the high frequency components of a transformed image. Next the
data concealed image is encrypted using Linde, Buzo and Gray (LBG) algorithm to
ensure security.  In 7, the electronic
code book (ECB) mode of advanced encryption standard (AES) is used for
encryption of patient record. The image is divided into region of interest
(ROI) and region of non interest (RONI). The discrete wavelet transform (DWT)
and inverse discrete wavelet transform (IDWT) are used for embeddin g encrypted
patient record in the RONI region.

 

In 8, the wavelet domain
watermarking technique is used to embed the Electronic health record in to the medical
image, there by generating a watermarked medical image. In the second stage of
the algorithm, a number of deoxyribonucleic acid masks are created using
logistic map function and DNA conversion rules. Then encryption is performed on
the watermarked image to generate a number of cipher images. Genetic algorithm
(GA) is applied to find the best DNA mask in iterative manner until the
condition is met.

 

Main FOCUS OF the CHAPTER

Issues, Controversies, Problems(Subhead 2:
Arial, Size 12, Title Case, Bold)

The digital medical image is usually very large in
size and contains very sensitive, confidential data. Providing the security to
the digital medical image and transmitting in less time is a big challenge. The
main aim of the research work contains design and development of efficient
methods to enhance the security level and improve the performance of the time
efficiency. 

The ancient techniques cannot survive every possible attack
and not robust. The DNA cryptography fulfills the requirements of digital
medical image due to its uniqueness. To enhance and provide high-level security
for digital medical image, multilevel security is proposed using Lorenz chaotic
map and DNA sequences.

 

BASIC
OPERATIONS

 

Lorenz Chaotic System

 

The Lorenz chaotic map is coined with butterfly effect
and very sensitive to initial conditions. The chaotic sequences generated are a
high dimensional and periodic. The sequences are very sensitive to initial
conditions and more complex. Hence it is suitable for digital medical images to
provide security and confidentiality. The Lorenz chaotic
system is defined in (1)-(3).

                                            P1=
?(Q?P)                                                            (1)

                                            Q1=
rP?Q?PR                                                       
(2)

                                           R1=
PQ?bR                                                            
(3)

where P, Q and R are arbitrary
parameters and ?=10, r=28 , b=8/3 are positive constants.

DNA Sequences

 

The basic elements of biological DNA are nucleotide,
because of the different chemical structure, nucleotide are divided into four
basic alphabets namely, Adenine (A), Guanine (G), Cytosine (C) and thymine (T).
Owing to the key hydrogen, the two chains are put together, and form a double
helix structure chain and that one chain in the base sequence complementary to
the other, that is, A and T are pairs , G and C are pairs as shown in Fig.1.

 

In the binary, 0 and 1 are complementary, therefore 00
and 11 are complementary, 01 and 10 are complementary. So that A, T, G and C
nucleic acid bases can be encoded as 00, 11, 10 and 01 respectively. Using this
concept we can get 4! = 24 different encoding patterns. But due to the

 

Table 1. Eight kinds of DNA
map rules

 

 

1

2

3

4

5

6

7

8

A

00

00

01

01

10

10

11

11

T

11

11

10

10

01

01

00

00

G

01

10

00

11

00

11

01

10

C

10

01

11

00

11

00

10

01

complementary relation between DNA bases only eight
patterns of encoding satisfy the complementary base pairing shown in Table 1.

 

PROPOSED
METHODOLOGY

 

Digital medical image encryption is performed by
combining the Lornenz chaotic system and DNA operation.  To enhance the security level in the proposed
selective medical image encryption algorithm multilevel encryption is used. In
the proposed model in first phase, the grayscale input digital medical image is
converted into an 8-bit binary image. The DNA sequence as A=01, T=10, G=11and
C=00 is applied on 8-bit binary image. The DNA encoded matrix is obtained. The
DNA encoded matrix is divided into 8 blocks and random pixels are selected. In
the second phase Lornez chaotic map with state variables and control parameters
are used to generate the chaotic sequence. The chaotic sequence is sorted and
based on index of the sorted chaotic sequence the selected pixels of the DNA
encoded matrix blocks are scrambled. In the third phase DNA add logical operation
is performed for the fusion of the scrambled DNA encoded matrix blocks to get
the intermediate encrypted image. The Table 2 shows the DNA add operation used
in this scheme. From Table 2 it is clear that the results of DNA add operation
is unique. The intermediate encrypted image is decoded using DNA sequence to
get final cipher image. The cipher image is decrypted using inverse process of
encryption and DNA sub operations as shown in Table 3 in place of DNA add
operation.

 

Table 2. DNA Add Operation

 

ADD

T

A

C

G

T

C

G

T

A

A

G

C

A

T

C

T

A

C

G

G

A

T

G

C

 

 

 

 

 

 

 

 

 

 

Table 3. DNA Sub Operation

SUB

T

A

C

G

T

C

G

T

A

A

A

C

G

T

C

T

A

C

G

G

G

T

A

C

 

 

 

 

 

 

 

 

 

 

The detailed steps
of selective digital medical image encryption are shown in Figure 2.

 

The selective
digital medical  image encryption process
is represented in Algorithm.1

 

Algorithm 1. Selective Digital Medical Image Encryption
(SDMIE)

Step 1: Start

Step 2: The grayscale input image is represented as I(m, n),
where m is  the row size and n is
column  size. Further, it is converted
into 8 bit binary image matrix of size m rows and n×8 columns

 Step 3:
The binary image is renovated into DNA encoded matrix as D(m, n)  of size m rows and n×4 columns

Step 4: The DNA encoded matrix as D(m, n) divided into 8 blocks

Step5:  The chaotic
sequences X,Y are generated using Lornez chaotic map. The chaotic sequences are
rearranged in increasing order as X1, Y1 to alter the pixel values

Step 6: The index value of 
X1 and Y1 chaotic sequences  are
used to scramble the randomly selected pixels of D(m, n) blocks .The
scrambled  pixels of the matrices are
represented as D1(m ,n)

Step 7: DNA add operation is used for the fusion of D1(m ,n)
blocks. The result of fusion is denoted as G(m ,n)

 Step
8:   Now transform the matrix G(m, n)  into decimal using Step 2 inversely and  encrypted digital medical image E(m ,n) is
obtained. This is the ciphered image 

Step 9:   Stop

 The decryption is performed using inverse
process of SDMIE algorithm and DNA sub operation.

 

PERFORMANCE ANALYSIS

A image
encryption algorithm has to resist all types of security analysis like
statistical attacks, differential attacks and exhaustive attacks. For
statistical attack, histogram analysis and correlation coefficient analysis are
used. The number of changing pixel rate (NPCR) and unified average changed
intensity (UACI) are used to check differential attack. The key space analysis
and key security analysis are used for exhaustive attack. The mean square error
(MSE) and peak signal to noise ratio (PSNR) are used to check the quality of encrypted
image.

 

Histogram  Analysis

 

The histogram
analysis shows the distribution of pixel value based on intensity. For
encrypted image based on the scatter of pixel value the cryptanalysis judge the
strength of image encryption algorithm.

Correlation Coefficient  Analysis

 

The
correlation coefficient is measure of correlation between the neighboring
pixels in the given images. The good encryption algorithm must have highly
correlated adjacent pixels. The Pearson’s correlation coefficient is shown in
eq. (4).

        

                                                          (4)

where I1
and I2 are the grayscale values of  the input digital medical image and encrypted digital
medical image. The N is size of the image. The value of r varies between +1 and
-1. A value equal to zero means no correlation 9.

 

NPCR and  UACI

The NPCR and
UACI are two decisive factor used to measure the performance of image
encryption methods against the differential attacks. The NPCR and UACI are
defined in eq.(5) and eq.(7) respectively.

 

                                                                                            (5)

where W1 and H1 are
width and height of the image and D1(i1,j1) is
defined as

 
                                                                          (6)

                                                                      

 
                                                                    (7)

where I1 and
I2 are two ciphered images respectively obtained from original image
and one pixel value changed original image.

 

MSE  and PSNR

 

The MSE and PSNR
are two metrics used to check whether the alteration of noise or error effects
the quality of the image. The MSE estimates the average of squares of the
errors between the input digital medical image ‘I1’ and encrypted  digital medical image ‘ I2 ‘9.
The MSE and PSNR are defined in eq. (8) and eq.(9) respectively.

   

                                                                                     (8)

      

                                                                                                             (9)

 

where N 
represents  size of the image.

 

EXPERIMENTAL RESULTS

The experimentation is carried out on digital medical
image. The Matlab (R2012a) software is used to implement the proposed method.
The Figure 2(a) shows the sample input digital medical image. In the proposed
SDMIE algorithm DNA map rule-2 as specified in Table 1 used to attain DNA
encoded odd matrix. In Lornez chaotic map the initial values of arbitrary parameters
are P=1.2, Q=1.2 and M=3.7 are considered to generate Lorenz chaotic sequence.
The Lorenz chaotic sequences are sorted and position of the sorted sequences is
used to scramble the randomly selected pixels of DNA encoded matrix. The ADD
operation specified in the Table 2 is used for the fusion of scrambled matrix to
get an encrypted digital medical image as shown in Figure 2 (b).

 

The performance analysis demonstrates that the
proposed algorithm provides high security. The Figure 3(a) shows the histograms
of the input digital medical image, Figure 3(b) shows encrypted digital medical
image and Figure 3(c) shows decrypted digital medical image. The histogram
analysis shows that the encrypted digital medical image
pixels are changed and it is equally distributed.

 

Key Space Analysis

 

In the proposed
SDMIE algorithm the primary values of state variables and the system parameters
of Lorenz chaotic maps are used as secret key. Thus, there are six secret keys
(K, L, M, ? , r, b) are used in proposed algorithm. The key size is 1011×1011×1011×1011×1011× 1011=1066, if
the precision is 1011. The secret key space is very huge to verify
exhaustive attack.

Key  Security Analysis

The
Lorenz chaotic system is highly very sensitive to initial conditions of state
variables and     control parameters. If
there is a slight modification then retrieving same input digital medical image
from decryption process is impossible. The secret key test is shown in Figure
4(a), where digital medical image is decrypted with wrong key q0=0.00000001
instead of q0=1 and histogram of decrypted digital medical image
with wrong key is shown in Figure 4(b). The Figure 4 (a and b) shows that the
decrypted digital medical image is not same as the input digital medical image
and the histogram of the decrypted digital medical image is comparatively
consistent. The other parameters (secret keys) are also very   sensitive. 
Hence proposed methodology is very sensitive to the keys. It shows that
SDMIE algorithm resist against the exhaustive attack.