performance analysis of medical image watermarking using dt … · 2018-07-29 · 2 faculty of...

1

Performance Analysis of Medical Image Watermarking Using DT CWT

and SVD Transforms With Steganalysis

1S.Priya, 2R.Varatharajan

1 Faculty of Electronics and Communication Engineering, Bharathiyar College of Engineering and Technology, Karaikal,

UT, Thiruvettakudy, Puducherry, Tamil Nadu, India

2 Faculty of Electronics and Communication Engineering, Sri Ramanujar Engineering College, Chennai, India

[email protected],

[email protected]

Abstract: Invisible watermarking plays an important role in medical field in order to embed the payload such as secret image or

text into source medical images. In this paper, watermarking is done on source medical image which uses Dual Tree Complex

Wavelet Transform (DT-CWT) transform and Singular Value Decomposit ion (SVD) transform. The DT-CWT transform is

applied over the cover image or source image in order to obtain low and high frequency sub band coefficients matrix. Next, SVD

transform is applied on the high frequency sub band coefficients, which is obtained from DT-CWT transformation. SVD is

applied on the payload which is in the format of either authentication image or text image. Embedded image is obtained by

coefficients matrix multiplication method (SVD coefficients of both cover and payload image) which also generates the key

pattern using the coefficients of SVD transformed sub bands. The performance of the proposed watermarking algorithm is

analyzed in terms of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Information Entropy (IE).

Key Words: Watermarking, medical filed, SVD, subbands, payload

I. INTRODUCTION

The reproduction of data and multimedia information

is important due to the rapid development of internet media

in the form of d igital media. Hence, the authorship is

required for every dig ital content which are passing in the

internet medium. In this regard, copyright protection is an

important topic to protect the d igital informat ion of the

individuals. Watermarking is such kind of copyright

protection methodology which is in the form of dig ital.

This methodology inserts the information of the ownership

such as emblem of the organization or details about

ownership into the source media which is to be secured

from the other individuals. Th is watermarking methodology

is categorized into visible and invis ible watermarking. In

case of visible watermarking, the embedded information

into the source data is visible to others. In case of invisible

watermarking, the embedded information is not visible to

others. In this mean t ime, the watermarking is also

categorized into lossy and lossless watermarking based on

the retrieval capability of the embedded information during

their recovery process. The quality of the image is affected

in case of visible watermarking due to the translucent

property of the embedded contents. In recent years,

numerous visible watermarking methodologies proposed.

Most of the methods [1, 2] used permanent visible

watermarking. Television media and digital library are the

International Journal of Pure and Applied MathematicsVolume 119 No. 17 2018, 891-902ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

891

2

best examples for v isible watermarking. In invisible

watermarking, the quality of the image is not affected by

the embedded contents. These kind of watermarking

methods are used for securing the dig ital information. The

process of watermarking in both cases of visible and

invisible is categorized into spatial domain method and

transforms domain method. In spatial domain method, the

intensity or color value of the pixels in source image is

modified with respect to pixels in watermark image. Least

Significant Bit (LSB) is the example for such method. This

method is not robust against various attacks. The

watermarking is performed in transformation mode in

transform domain method. The transformation such as

Discrete Wavelet Transform or Singular Value

Decomposition (SVD) is applied on source and watermark

images. The watermark image is embedded into

transformed image and then inverse transform is applied to

get the watermarked image. It is robust to various noises

and attacks.

Fig. 1 shows the generic arch itecture of watermarking

framework, which embeds the watermark into cover image.

Fig. (1). Generic watermarking framework.

This paper is structured as, section 2 exp lains the

conventional methodologies for invisible watermarking,

section 3 proposes a framework for invisible watermarking

using mult i resolution transforms, section 4 discusses the

experimental results and section 5 concludes the paper.

II. LITERATURE SURVEY

Salama et al. (2016) p roposed hybrid fusion technique

for watermarking the secret image into source image. This

methodology used Discrete Cosine Transform (DCT) and

Discrete Wavelet Transform (DWT) for key based

watermarking method in mult i resolution transform mode.

The imperceptibility of the watermarking technique was

improved by adopting and combining these two

transformation modes. J. Singh et al. (2016) used wavelet

transform based watermarking methodology. The DWT

transform was applied on the source and watermark images

and then LL sub band was obtained from both images.

Next, DCT transform was applied on LL sub band images

and scaling factor was adjusted in order to embed the

watermark image into source image. The authors achieved

33.45 dB PSNR for their proposed algorithm. Maninder

Kaur et al. (2016) used both spatial domain and frequency

domain methods for embedding the watermark image into

Cover Image

Watermarking

Algorithm

Watermark

Image

Embedded

Image

International Journal of Pure and Applied Mathematics Special Issue

892

3

source image. The authors used least significant bit

technique to embed the watermark image into source

image. The authors achieved 37.6758 dB PSNR for their

proposed method.

Srilakshmi et al. (2016) used DWT and SVD t ransform

for watermarking the secret image into source image. The

DWT transform was applied on the source image and SVD

transform was applied on the secret image in order to obtain

coefficients. These coefficients were embedded us ing the

scaling factor by adjusting the values in coefficient matrix.

Tsung-Yuan et al. (2010) developed an algorithm for

lossless visible watermarking in both cases of generic and

translucent categories. The authors used two-fold

monotonically technique to overcome the overflow pixel

problem during watermarking process. The watermarking

process was done in spatial domain which embeds the

generic and translucent watermarks directly into source

image. The authors tested their proposed watermarking

algorithm on d ifferent standard benchmark source and

watermark images. Li et al. (2010) proposed saliency based

watermarking methodology using multi resolution

transformation. The authors used wavelet transform for

embedding the watermark image into source image. The

wavelet transform was applied on the source and watermark

images and then orientation map was constructed on each

subband of the wavelet transformed images.

The following points are observed from the literature

survey. They are listed below as,

Most of the watermarking techniques were based on

DWT and SVD.

No noise reduction technique was used during

watermarking process.

Steganalysis was not performed in most of the

conventional methods.

III. MATERIALS AND METHODS

A. Materials

In this paper, brain images are obtained from open

access dataset BRATS (Multimodal Brain Tumor

Segmentation) challenge. This dataset contains various

MRI modalities brain images in various categories. This

dataset have 230 brain MRI images which are categorized

into normal which do not contain any abnormal t issues and

abnormal which contain abnormal lesions . In this paper,

100 brain images (70 from normal category and 30 from

abnormal category) are used as source or cover images.

B. Methods

The proposed watermarking and its ext raction

procedure using multi resolution transforms are detailed in

Fig. 2. The DT-CWT transform is applied over the cover

image or source image in order to obtain low and high

frequency sub band coefficients matrix. Next, SVD

transform is applied on the h igh frequency sub band

coefficients, which is obtained from DT-CWT

transformation. SVD is applied on the payload which is in

the format of either authentication image or text image.

Embedded image is obtained by coefficients matrix

multip licat ion method (SVD coefficients of both cover and

payload image) which also generates the key pattern using

the coefficients of SVD transformed sub bands. This key is

used in receiver side in order to extract the payload.


893

4

Fig. (2). Proposed watermarking and its extraction methodology using multi resolution transforms.

Fig. (3). (a) Cover image (b) Payload image.

Fig. 3(a) shows the cover or source image which is

obtained from open access dataset and Fig. 3 (b) shows the

payload image which is the text image or authentication

image.

Dual Tree- Complex Wavelet Transform

In the DT-CWT, the cover image is passed through the

low pass filter (LPF) and high pass filter (HPF) as shown in

Fig. 4. The filtered sub bands are down-sampled by a factor

of 2. This process converts the cover image into low pass

(Approximate) and high pass (Detail) coefficients. This

procedure forms the first level of decomposition of the

cover image. For the next level of decomposition, the

approximate coefficients are again passed through the HPF

and LPF, and the same process is repeated to obtain the

next level of filter coefficients . After each level of

decomposition, the bandwidth obtained is half of the

bandwidth of the previous level.


894

5

Fig. (4). Decomposition of cover image using 4-level DTCWT.

In this paper, the cover image is subjected to the DT-

CWT for up to four levels of decomposition. The dual tree

approach uses two real DWTs: one for acquiring the real

part of the transform, and the other for the imaginary part.

Real wavelet is associated with the upper tree, and

imaginary wavelet is associated with the lower t ree.

Each t ree uses different sets of filters that satisfy perfect

reconstruction conditions. The h0(n) is a low-pass and h1(n)

is a high-pass filter for the upper filter bank, and g0(n) and

g1(n) are the low and the high-pass filters for the lower

filter bank. The relat ion between upper filter and lower

filter bank is a half-sample delay as described in Eqn. (1).

(1)

The dual tree CWT decomposes the cover image into

complex wavelet and scaling function. The complex

wavelet function and its scale function of DT-

DWT are represented as,

(2)

(3)

Where, and denotes real and imaginary wavelet

function and and denotes real and imaginary

scale function.The time domain transformation is

represented by ‘t’. The real and imaginary parts of the

complex wavelet function is expressed as,

(4)

(5)

The real and imaginary parts of the complex scale

function is expressed as,

(6)

(7)

denotes the low pass filter of the complex scale

function and denotes high pass filter of the complex

wavelet function.

The proposed watermarking methodology using DT-

CWT is carried out using MATLAB DT-CWT toolbox. The

extracted dual tree complex wavelet coefficients of fourth

level provide a compact representation of the cover image

at different frequency bands which represents the

distribution of frequencies components of cover image in

both time and frequency domain. Fig. 5 shows the sub band


895

6

images of cover image using 4-Level DT-CWT decomposition.

Fig. (5). Sub band images of cover image using 4-Level DT-CWT decomposition.

Fig. 6 shows the SVD transformed sub band images on

high frequency coeffieincts of DTCWT transform. The sub

band image (S) has mutual and optimum information when

compared with other sub bands. Hence, ‘S’ sub band image

of both cover and payload images are used to produce

embedded image.

Fig. (6). SVD components of Cover image (S, U and V).


896

7

Algorithm1: Embedding procedure of cover image with payload

Inputs: High frequency coefficients sub bands of DT-CWT (w1 and w2)

Output: Embedded image

Start

Determine low pass scaling factor using LS=0.09* w1

Determine high pass scaling factor using HS=0.09* w2

Apply SVD on LS and HS coefficients using the following procedure

[U1 S1 V1] = SVD (LS)

[U2 S2 V2] = SVD (HS)

Find Apha1 factor as α1= U1 * LS* V1.

Find Apha2 factor as α2= U2 * HS* V2.

Apply the same procedure on payload image in order to obtain the alpha factors.

Perform matrix multiplication between alpha factors of source and payload images.

Apply inverse DT-CWT transform on low frequency coefficients of DTCWT (low1) and alpha factors as,

Embedded image = icplxdual2D (low1, α1, α2).

End

Fig. (7). (a) Cover brain image (b) Embedded cover brain image.

Fig. 7(a) shows the cover brain image and Fig. 7(b)

shows the embedded cover brain image which has the

inbuilt payload information. It is clear from Fig. 7(a) and

Fig. 7 (b), both images are similar at perception view of

reader. The indexes of coefficients U1, S1, V1 and U2, S2,


897

8

V2. The reverse procedure is applied on the embedded

image in o rder to obtain the payload image in receiver side.

Fig. 8 shows the extracted payload image from embedded

image.

Fig. (8). Reconstructed payload image

(62x85mm (150 x 150 DPI))

Steganalysis

The performance of the proposed watermarking

methodology is improved by detecting and removing the

impulse noises (salt and pepper noise) from the

watermarked image through steganalysis process. For the

analysis purposes, various intensities of impulse noises

which range from 0.1 to 0.9 are added into the embedded

image. This process is explained in the following steps.

Step 1:

Choose m*m pixels in watermarked image. The

number of row and column p ixels in sub window of

watermarked image is represented by m. The ‘m’ value

should be odd number to have the equal number of pixels in

both side of the current pixel which is to be denoised.

Fig. (9). Illustration of selection of directional pixels.

Step 2:

Select four directional pixels such as D1, D2, D3 and D4 as

shown in Fig. 9 through the center pixel which is to be

denoised.

Step 3:

Sort the pixels in each directional pattern after removing the

center pixel which is to be denoised.


898

9

Step 4:

Remove the first and last pixels in each directional pattern.

Step 5:

Determine standard deviation of each d irect ional pattern

pixels and find the directional pattern which has low

standard deviation.

Step 6:

Find the scalar value (S) between each pixels in directional

pattern and the center pixel to be denoised using the

following equation as,

(8)

Where as, Dop

is the pixels in directional pattern and Xcp

is

the center pixel which is to be denoised.

Step 7:

Classify the center pixel into either noisy or noise free

based on the following criteria.

(9)

Where as, xor is the noise free pixel and xno is the noisy

pixel. The threshold value is represented by T and K is the

window size.

Step 8:

Apply adaptive median filter (Jiang et al. 2010) on

center pixel if it is classified as noisy pixel.

IV. RESULTS AND DISCUSSION

In this paper, MATLAB R 2014 is used as simulation

software to simulate the proposed watermarking and its

extraction methodology using mult i resolution transforms.

The brain images which are obtained from open access

dataset are used in this paper as source or cover image. In

this paper, 100 brain images are used as cover images and

brain or authentication images are used as payload images.

The performance of the proposed invisible watermarking

system using multi resolution transforms is analyzed in

terms of PSNR, MAE, MSE, IE, Bhattacharya Coefficient

and Normalized Histogram Coefficient.

A. PSNR and MSE

This parametric evaluates the quality of the extracted

payload image with respect to original payload image using

the following equations (10) and (11) as,

(10)

(11)

Where, ‘ ’ represents width of the original payload

image and ‘ ’ represents height of the original payload

image, respectively. is the original payload image

and is the extracted payload image, respectively.

B. Mean Absolute Error (MAE)

It defines the percentage of erro r in extracted payload

image with respect to original payload image and it is given

as,

(12)

C. Normalized Histogram Intersection Coefficient

(NHIC)

This performance metric gives count of the same value

of pixels between two h istograms. If the probability

distribution of two images is taken as P and Q respectively,

then Normalize Histogram Intersection coefficient is given

by,

(13)

Where A is original payload image and B is extracted

payload image. The range of value for this coefficient is

between 0 to1. Where 0 represents mismatch and 1

represents exactly match.


899

10

D. Information Entropy (IE)

The information entropy (EN) is computed for the

decoded original source image. Its value should be 8 for

grey scale image. The security level of proposed system is

high if the value of information entropy is equal to 8.

(14)

Where, p(mi) represents the probability of the symbol mi

and t is the total number of symbols.

Table 1 shows the Performance analysis of the

proposed watermarking methodology over the set of 100

brain MRI images as source or cover images with different

payload images.

Table 1. Performance analysis of the proposed watermarking

methodology.

Performance evaluation metrics Experimental results

PSNR 53.32 dB

MSE 138.01

MAE 11.73

NHIC 0.001282

IE 7.3

The proposed watermarking algorithm achieves 53.32

dB of PSNR, 138.01 of MSE, 11.73 MAE, 0.001282 NHIC

and 7.3 IE, as depicted in Table 1.

Table 2. Performance comparisons of the proposed method

with conventional methods.

Methodology Year PSNR (dB)

Proposed method 2017 53.32

(DWT+SVD)

Hector Santoyo-Garcia et al.

(Bayer method)

2017 15.96

J. Singh et al.

(DWT+DCT)

2016 33.45

Kaur et al.

(Spatial+frequency domain)

2016 37.675

Table 2 shows the performance comparisons of the

proposed watermarking methodology with conventional

watermarking methodologies as Singh et al. (2016) and

Kaur et al. (2016). The proposed method used in this paper

achieves 53.32 dB of PSNR while other conventional

methods as Singh et al. (2016) achieved 33.45 dB of PSNR

and Kaur et al. (2016) achieved 37.67 dB of PSNR. Hector

Santoyo-Garcia et al. (2017) used Bayer method for

embedding the watermark logos into source image. The

image quality in the retrieval process was affected due to

the instability and non robustness of the method. Hence, the

authors achieved 15.96 dB of PSNR. Singh et al. (2016)

used the combinations of digital transformat ions DWT and

SVD for embedding the watermark pixels into source

image p ixels. The robustness of the watermarked image is

affected by using frequency domain transformation

techniques alone. This method obtained 33.45 dB of PSNR.

Kaur et al. (2016) used the integration transformation

approaches such as spatial and frequency domain

techniques for embedded process.

V. CONCLUS ION

In this paper, watermarking is done on source medical

image which uses Dual Tree Complex Wavelet Transform (DT-

CWT) transform and Singular Value Decomposition (SVD) transf

orm. The performance of the proposed watermarking algorithm is


900

11

analyzed in terms of PSNR, MSE and Informat ion Entropy. The

proposed watermarking algorithm stated in this paper achieves

53.32 dB of PSNR and 7.3 IE.

REFERENCES

[1] Dimitrios Simitotoulos et al. Robust Image Watermarking based

on Generalized Radon Transformations. IEEE Transactions on

Circuits and Systems for Video Technology 2003; 13(8): 732-745.

[2] Hector Santoyo-Garcia, Eduardo Fragoso-Navarro, Rogelio

Reyes-Reyes, Clara Cruz-Ramos, and Mariko Nakano-Miyatake.

Visible Watermarking Technique Based on Human Visual System

for Single Sensor Digital Cameras. Security and Communication

Networks 2017; 2017(7903198): 1-18.

[3] Hu, Y, Kwong, S, Huang, J. An algorithm for removable visible

watermarking. IEEE Transactions on Circuits and Systems for

Video Technology 2006; 16(1): 129–133.

[4] Jiang, J, Shen, J. An Effective Adaptive Median Filter Algorithm

for Removing Salt & Pepper Noise in Images. Symposium on

Photonics and Optoelectronics, Chengdu, 2010; 1-4.

[5] Li, ZQ, Fang, T, Huo, H. A saliency model based on wavelet

transform and visual attention. Sci. China Inf. Sci. 2010; 53(4):

738–751.

[6] Lin, Y, Chen, YH, Chang, CC, Lee, JS. Contrast -adaptive

removable visible watermarking (CARVW) mechanism. Image

and Vision Computing 2013; 31(4): 311–321.

[7] Maninder Kaur, Nirvair Neeru. Digital Image Watermarking using

New Combined Technique. International Journal of Computer

Applications 2016; 145(2).

[8] Menze, B, et al., The multimodal brain tumor image segmentation

benchmark (Brats). IEEE Transactions on Medical Imaging 2015;

34(10): 1993–2024.

[9] Minh Do, Martin Vitterli. The Contourlet transform: An efficient

directional multi resolution image representation. IEEE

transactions on Image Processing 2005; 14(12).

[10] Salama, AS, Mokhtar, MA. Combined technique for improving

digital image watermarking. 2nd IEEE International Conference

on Computer and Communications (ICCC), Chengdu, China,

2016; 557-562.

[11] Singh, J, Patel, AK. An effective telemedicine security using

wavelet based watermarking. IEEE International Conference on

Computational Intelligence and Computing Research (ICCIC),

Chennai, India, 2016; 1-6.

[12] Srilakshmi, P, Himabindu, C. Image watermarking with path

based selection using DWT & SVD. IEEE International

Conference on Computational Intelligence and Computing

Research (ICCIC), Chennai, India, 2016; 1-5.

[13] s.p. jeyakokila1 , p. sumathi2, on soshearenergy of a tree of

diameter 4 part iii, international journal of pure and applied

mathematics, 2017,112 (5), 9-21

[14] tsung-yuan liu, wen-hsiang tsai. generic lossless visible

watermarking—a new approach. ieee transactions on image

processing 2010; 19(5).


901

performance analysis of medical image watermarking using dt … · 2018-07-29 · 2 faculty of...

Documents