Robust Zero-Watermarking for Medical Image Based onDCT Chunhua Dong
Jingbing Li
College of Information Science and Technology Hainan University Haikou, China
[email protected]
College of Information Science and Technology Hainan University Haikou, China
[email protected]
Huaiqiang Zhang
Yen-wei Chen
College of Information Science and Technology Hainan University Haikou, China
[email protected]
College of Information Science and Engineering Ritsumeikan University Kasatsu-shi , Japan
[email protected] (DCT, DFT and DWT), which can be implemented by changing some pixel grayscale values in the space domain or by changing the values of coefficients in the transform domain to embed watermark. Due to the smaller computational and compatible with the international standards of data compression (JPEG, MPEG), DCT (Discrete Cosine Transform) has been a hot topic for the digital watermarking in frequency domain.
Abstract—When designing watermarking algorithm of the medical image, one of the principal problems faced is how to determine the region of interest (ROI). This paper proposes a zero-watermarking algorithm of the medical image using DCT, which can effectively solve this problem. This algorithm can enhance medical image security, confidentiality and integrity in the application for the clinical. The algorithm combines the visual feature vector of images, encryption technology with the third party authentication, and avoids the selection of ROI to speed up watermarking embedding and extracting. The simulation results demonstrate the algorithm has desired robustness against common attacks and geometric attacks, such as JEPG, rotation, scaling and translation, etc.
Regarding to the special requirements of the medical image's lesion protection, the general approach of the medical digital watermarking often embeds the watermarking into the Region Of Non-Interest (RONI). The ROI of the medical image refers to the area of lesion that contains the important pathological features. If the embedded watermarking was placed in these regions, it may cause an erroneous diagnosis. At the same time, people often spend much time and energy on looking for ROI, and it is possible to interfere the doctor's diagnosis if the selection is wrong.
Keywords- DCT; Medical image; Digital watermarking; ROI
I. INTRODUCTION With the advancing of the multimedia technology and digital image processing, digital imaging technology has been widely infiltrated into the field of medical and become a very important auxiliary tool of the clinical diagnosis. However, when CT, MRI and other medical images are transmitted on the Internet, the patients’ personal information can be easily compromised. Current, encryption and access control technologies are difficult to meet the requirements of the medical image’s information security. Hence, a new measure of information security technology is of great urgency. The watermarking technology can be an effective way to solve this problem. This kind of technology put patients' information as a digital watermarking hidden in medical images. When the medical images have been attacked through JEPG compression, filtering, rotation, scaling and others, it can still completely extract the watermarking, to protect patients' personal information. It has a high research impact on determining disease, operation design, patient communication, medical education and other aspects.
This paper proposed a new algorithm which can avoid the tedious process for selecting ROI, and balance the conflict between invisibility and robustness. The process of the watermarking embedding is designed as follows: first, to obtain a feature vector of the medical image by DCT, then use the feature vector and watermarking information to generates a sequence of binary logic as the key by the HASH function, and finally store the key for watermarks detection. The process of the watermarking extraction is designed as follows: First, to obtain a feature vector of the medical imageby DCT, which is the same one as in embedding procedure, and then use this feature vector and the stored key in the embedding procedure for extracting the hidden watermarking sequence by HASH function. The result of experiment indicates that the algorithm can achieve a true embedded zero-watermarking. Meanwhile, it has a strong robustness against common attacks and geometric attacks.
Currently the field of digital watermarking for medical research focused on the spatial domain and transform domain
900
proved that the sequence of the DCT coefficient signs can reflect the main visual characteristics of medical images.
II. THE DISCRETE COSINE TRANSFORM (DCT) A. The discrete cosine transform (DCT) The discrete cosine transform is a signal analysis theory, which is widely used in JPEG and MPEG compression standard. It is well known due to its operation speed and high precision.
III.
We choose a group of independent binary pseudomorph sequences as the watermarking. The group of sequences is described as: W= {w(j) | w(j)=0 or 1; 1İjİL}. At the same time, we select the tenth slice of one medical volume data as the original medical image. It is described as: F= {f (i,j), 1İi İ N1, 1 İ j İ N2}, where f (i,j) denotes the value of the original medical image at the point (i,j). To facilitate the operation, we assume N1=N2 = N.
The M u N medical image’s DCT is defined by: F (u , v )
M 1 N 1 P 1 (2 x 1)uS (2 y 1)vS cos ] c (u ) c ( v ) [ ¦ ¦ ¦ f ( x , y ) cos x 0 y 0p 0 2M 2N
u
c (u )
0, 1, ..., M 1;v
° 1 / M u 0ǂǂ ǂǂ ǂ ǂǂ ˗ ® u 1, 2, ..., M 1 °¯ 2 / M
(1)
0, 1, ..., N 1 c (v )
1/ N v ǂǂ ® v ¯ 2/N
0 ǂǂǂǂǂ 1, 2 ,..., N 1
A. Watermark embedding algorithm VWHS Acquire the feature vector of the original medical image. First, DCT of the whole F(i,j) is computedas the DCT coefficient matrix, FD(i,j). Then, after arranging the DCT coefficients from low to highfrequency, the low-frequency sequence Y (j) can be obtained. Finally, the feature vector V= {v(j) | v(j)=0 or 1; 1İjİL} , can be achieved as a signs sequence of the top L values in the low-frequency Y(j) by symbolic computation. Where the value of L can tune the robustness and capability of the embedded watermarking (in this paper we set L=32=4x8 bits). The process can be described as follows: FD (i , j ) DCT 2( F (i , j )) (2) Y ( j ) Zig _ Zag ( FD (i , j )) (3) V ( j ) Sign(Y ( j )) (4) VWHS Acquire the key sequence. Utilizing the watermarking W and the feature vector V, we can generate the key sequence, Key(j)as follows: Key ( j ) V ( j ) W ( j ) (5) Where V(j) denotes the feature vector of the original medical image, W(j) denotes the watermarking to be embedded. The binary key sequence, Key(j), can be computed by the HASH function of cryptography. The Key(j) should be stored for extracting the embedded watermarking later. Furthermore, Key(j) can also be regarded as a key and registered to the third part to preserve the ownership of the original image .
Where x, y is the spatial domain sampling; u, v is the frequency domain sampling. Digital image pixels are usually square, i.e. M = N. B. The feature vector designing of medical image The original image is computed using DCT. We choose 7 low-frequency coefficients (F(1,1), F(1,2) …F(1,7)) for formation of the feature vector, shown in Table I. We find that the value of the low-frequent coefficients may change after the image has undergone an attack, particularly geometric attacks. However, the signs of the coefficients remain unchanged even with strong geometric attacks, as also shown in Table I. Let “1” represents a positive or zero coefficient, and “0” represents a negative coefficient, and then we can obtain the sign sequence of low-frequency coefficients, as shown in the column “Sequence of coefficient signs” in Table I. After attacks, the sign sequence is unchanged, and the normalized crosscorrelation (NC) is equal to 1.0. TABLE I CHANGE OF DCT LOW-FREQUENCY COEFFICIENTS WITH RESPECT TO ATTACKS
Image Process
Sequence of F(1,1) F(1,2) F(1,3) F(1,4) F(1,5) F1,6) F(1,7) (102) (102) (102) (102) (102) (102) (102) coefficient NC signs
Original image
31.65 0.64 -26.35 -6.95 -0.09 4.84 -13.03 1100 010 1.0
Gaussian noise (2%) JPEG (4%); Median filter [3x3] Rotation (20°) Scaling (x0.5) Translation (7%down) Cropping (10%, from y )
DCT BASED WATERMARKING FOR MEDICAL IMAGE
42.88 0.87 -20.37 -4.72 -0.26 3.80 -9.46 1100 010 1.0 32.73 0.95 -23.40 -5.86 -0.08 3.85 -11.49 1100 010 1.0
B. Watermark extraction algorithm VWHS Acquire the feature vector of the tested image. This process of acquiring the feature vector T_V is same to step 1 of the watermarking embeddingprocess. The obtained feature vector, T_V = {t_v(j) | t_v(j)=0 or 1; 1İjİL}, also consists of the signs sequence of the DCT coefficients, where L has the same meaning as previously. The process can be described as follows: (6) T _ FD i , j DCT 2 T _ F i , j
31.87 0.62 -26.69 -7.13 -0.10 4.99 -13.02 1100 010 1.0 31.65 3.17 -24.48 -6.40 -4.10 3.59 -14.39 1100 010 1.0 15.87 0.33 -13.16 -3.47 -0.04 2.41 -6.51 1100 010 1.0 30.72 0.61 -25.16 -12.23 -0.19 8.42 -11.08 1100 010 1.0
31.27 0.65 -25.40 -10.27 -0.18 6.29 -9.99 1100 010 1.0
T _ Y ( j)
This means that the signs of the sequence can be regarded as the feature vector of the medical image. Furthermore, it
Zig _ Zag (T _ FD (i , j ))
T _ V ( j)
Sign(T _ Y ( j ))
VWHS Extracting watermarking.
901
(7) (8)
The watermarking can be extracted as follows: (9) W '( j ) T _ V '( j ) Key ( j ) where W’(j) is the extracted watermarking, T_V(j) is the feature vector of the tested image, and Key(j) was the stored one obtained from the above embedding watermarking process. Then the value of NC is being computed between W and W’. Thus, we can extract the hidden information from the image. Furthermore, the hidden information (watermarking) can be extracted without the original data, which is advantageous to protect the safety of the medical image. C. Evaluation measure of the proposed watermarking algorithm The normalized cross-correlation (NC)is used for measuring the quantitative similaritybetween the extracted and original (embedded) watermarkings, which is defined as: ¦ ¦ W i, j W ' i, j i j NC (10) 2 ¦ ¦ W i, j i j where W denotes the embedded watermarking and W’ denotes extracted watermarking. The higher the NC value, the more similarity there is between the embedded and extracted watermarking. The peak signal to noise ratio (PSNR) is used for measuring the distortion of the watermarked image, which is defined as: 2 º ª I i, j « MN max » i, j « » PSNR 10 lg (11) 2
« «¬ ¦i ¦j I i, j I' i, j
medical image after watermarking has no change, and the human eye can’t see the traces of the watermarking. However, the embedded watermarking can be detected clearly. Therefore, it fully meet with the requirements of the invisibility of the watermarking.
(b)
(a)
Fig. 1. The watermarked medical image without attacks: (a) the original medical image; (b) watermarking detector
$ Common attacks Adding Gaussian noise. In the watermarked image, Gaussian noise is added by the imnoise( ) functionwith different noise level. The medical image under the attack of Gaussian noise (3%) with PSNR=8.02dB is shown in Fig. 2(a). As shown in Fig. 2(b), the watermarking can obviously be detected with NC=0.94. Table II shows the NC values between the extracted and embedded watermarkerings, and the PSNR of the attacked watermarked images, which prove that our proposed algorithm has strong robustness against noise attacks.
»»¼
where I(i,j), I'(i,j) denote the pixel gray values of the coordinates (i, j) in the original image and the watermarked image, respectively; M, N represent the image row and columnnumbers of pixels, respectively. IV.
(a)
EXPERIMENTS
(b)
Fig. 2. Under noise attacks (3%): (a) an image under noise attack; (b) watermarking detector.
To verify the effectiveness of our proposed algorithm, we carried out the simulation in Matlab2010a platform. In the experiments, 1000 groups of the independent binary pseudomorph sequences are used. One of 1000 groups W={ w(j) | w(j)=0 or 1, 1İjİ32}is randomly selected as the embedded watermarking (in this paper, the 500th group is selected). Fig. 1(a) shows the original medical image F={f (i,j), 1İiİ128, 1İjİ128}.
TABLE II THE PSNR AND NC UNDER NOISE ATTACKS
Noise parameters 1 (%) PSNR(dB) 12.39 NC
0.94
3
5
10
15
20
25
8.02
6.00
3.31
1.77
0.84
0.08
0.94
0.88
0.83
0.82
0.81
0.77
JPEG attacks
In this simulation, PSNRis used for objectively evaluating the quality of the watermark image, and the NCis used for objectively evaluating the results of watermark detection.
JPEG compression process is done by using the percentage of image quality as a parameter to measure. The medical image with PSNR=17.61dB under JPEG attacks (4%) is shown in Fig. 3(a). As shown in Fig. 3(b), the watermarking can obviously be detected with NC=0.81. Table III gives the PSNRs and NCvalues for watermarking extraction measure under different JPEG compression quality. The watermarking
Fig. 1(b) is the NC values between the 1000 available pseoudomorph sequences (the 500th is the embedded one) and the extracted watermarking, which is achieved using the stored key and the watermarked image (PSNR = 81.03dB)without any external interference. . It can be seen from Fig. 1 that the
902
can still be detected. The results show that the watermarking algorithm has strong robustness against JPEG attacks.
(a)
by rotation and the NC values between the embedde and extracted watermarkings with by multiple rotation parameters, which can prove that our proposed algorithm has strong robustness against rotation attacks.
(b)
Fig. 3. Under JPEG attacks (4%): (a) an image under JPEG attack; (b) watermarking detector.
Fig. 5. Under rotation attacks (angle 20): (a) an image with rotation attack; (b) watermarking detector.
TABLE III THE PSNR AND NC UNDER NOISE ATTACKS
Compression Quality(%) PSNR(dB) NC
4
8
17.61 0.81
19.99 0.94
10
20
40
(b)
(a)
60
80
TABLE V THE PSNR AND NC UNDER ROTATION ATTACKS
20.98 23.04 25.06 26.52 29.27 0.71 0.75 1.0 1.0 1.0
Filter Processing. We also explore the filter impact on the watermarked medical image with different size of median filter and the repeat number of filtering.The medical image under median filter attacks[3x3] is shown in Fig. 4(a)and has the PSNR value 21.30dB. As shown in Fig. 4(b), the watermarking can obviously be detected with NC=0.88. Table IV gives the PSNR and NC under different median filter parameters. The results show that the watermarking algorithm has strong robustness against median filter attack.
(a)
Rotation PSNR(dB)
5嘙
10嘙
15嘙
16.19
13.49
12.70
12.38 12.16 11.90 11.58
20嘙
25嘙
NC
0.88
0.83
0.83
0.83
0.79
30嘙 0.75
35嘙 0.79
Scaling attacks We use the scaling factor as parameter to validate the effectiveness of our proposed algorithm on different scalling attacks. Fig. 6(a) shows that the medical image shrunk with a scale factor of 0.5. Moreover, Fig. 6(b) shows that the watermarking can be detected with NC=1.0. Table VI shows the NC values between the embeded and extracted watermarkings with scaling attacks on the watermarked images with multiple scale parameters, which can prove that our proposed algorithm has strong robustness against scaling attacks.
(b)
Fig. 4. Under filter attacks ([3x3], 20 times): (a) an image under filter attack; (b) watermarking detector.
(b)
(a)
TABLE IV THE PSNR AND NC UNDER FILTER ATTACKS
Media filter[3x3] Media filter [5x5] Media filter [7x7] Repeat times 1 10 20 1 10 20 1 2 10 PSNR(dB) 24.52 21.85 21.30 20.44 18.28 17.73 18.41 16.99 16.91 NC 0.94 0.94 0.88 0.94 0.81 0.75 0.87 0.69 0.69
Fig. 6. Under scaling attacks (factor 50%): (a) an image under scaling attack; (b) watermarking detector.
% Geometrical attacks Rotation attacks
Scaling factor
0.2
0.5
0.8
1.0
1.2
2.0
4.0
NC
0.87
1.00
1.00
1.0
1.00
1.00
1.00
TABLE VI THE NC UNDER SCALING ATTACKS
Translation attacks
We invesitigate the effectiveness of our proposed watermarking algorithm against ratation attacks with rotation angle as the parameter. The medical image under rotation attacks(clockwise by 20), which has the PSNR walue 12.38 dB, is shown in Fig. 5(a). As shown in Fig. 5(b), the watermarking can obviously be detectedwith NC=0.83. Table V gives the PSNR values of the attacked watermarked image
The translation attacks are added to the watermarked image for validating the effectiveness of our proposed algorithm. Fig. 7(a) shows that the medical image translated by 10 % vertical translation down, which has the PSNR value 12.69dB . Moreover, as shown in Fig. 7(b), the watermarking can obviously be detected with NC=0.81. Table VII gives the
903
TABLE VIII
PSNR values of the attacked watermarked images and the NC values between the embeded and extracted watermarkings aftet translation attacks with multiple parameters, which can prove that our proposed algorithm has strong robustness against translation attacks.
(a)
THE NC UNDER CROPPING ATTACKS
Cropping ratio NC(from Y)
2% 1.00
4% 0.94
8% 1.00
10% 1.00
20% 0.87
30% 0.76
40% 0.64
V. CONCLUSION This paper proposes a zero-watermarking algorithm of the medical image by using DCT, which fully considers the human visual system(HVS) characteristics in the process of the watermarking embedding, innovative usage of the zerowatermarkingconcept. By combining the visual feature vector of images, the encryption technology and the third-party conception, the proposed watermarking algorithm has not only a very strong robustnessagainst different attacks but also no affect on the quality of the original medical image. Therefore, it is very practical to be used in health care system. Moreover, the proposed watermarking algorithm can be used to protecting the medical images, and then can be applied to general images, digital audio, digital video and other areas.
(b)
Fig. 7. Under translation attacks (down 20q): (a) an image with translation attack; (b) watermarking detector. TABLE VII THE PSNR AND NC UNDER TRANSLATION ATTACKS
Horizontal translation (Left)
ACKNOWLEDGMENT
Vertical translation (Down )
Distance (pixels)
6
8
10
6
8
10
PSNR (dB)
11.27
10.80
10.31
12.33
11.96
11.69
NC
0.94
0.69
0.69
0.87
0.87
0.81
This work is partly supported by 211 Project, by Hainan University Graduate Education Reform Project (yjg0117), and by Natural Science Foundation of Hainan Province (60894), and by Education Department of Hainan Province project (Hjkj2009-03), and by Communication and Information System, Hainan University -- Institute of Acoustics, Chinese Academy of Sciences for Joint Training of Special Support.
Cropping attacks The cropping attacks are added to the watermarked image for validating the effectiveness of our proposed algorithm. Fig. 8(a) shows that the medical image cropping from y axis with the ratio of 20 %. Moreover, Fig. 8(b) shows that the watermarking with NC=0.87 can be detected. Table VIII gives the NC values between the embeded and extracted watermarkings with cropping attacks on the watermarked images with multiple different parameters, which can prove that our proposed algorithm has strong robustness against cropping attacks.
REFERENCES [1]
[2] [3]
[4]
[5]
[6]
(a)
(b)
[7]
Fig. 8. Under cropping attacks (From the Y axis, 20%): (a) an image with cropping attack; (b) watermarking detector.
904
K. A Navas and M. Sasikumar, “Survey of Medical Image Watermarking Algorithms,” in Proc. of the 4th Sciences of Electronic, Technologies of Information and Telecommunications International Conference, Tunisia, pp. 25-29, March 2007. Jingbing Li, ̌ Digital watermarking algorithms robust to geometrical attacks,̍ Beijing, 2007㧚 X. Q. Zhou, H. K. Huang, S. L. Lou, “Authenticity and integrity of digital mammography images,” IEEE Trans. on Medical Imaging, vol. 20, Issue 8, pp. 784–791, 2001. A. Wakatani, “Digital watermarking for ROI medical images by using compressed signature image,” in Proc. of the 35th Annual Hawaii International Conference on System Sciences, pp. 2043–2048, Jan 2002. Cox I J, Kilian J, Leighton F T, ̌Secure spread spectrum watermarking for multimedia,̍ IEEE Trans. on Image Processing, vol. 6, Issue 12, pp. 1673-1687, 1997. X. Kong and R. Feng, “Watermarking medical signals for telemedicine,” IEEE Trans. on Information Technology in Biomedicine, vol. 5, Issue 3, pp. 195–201, Sept 2001. W. Puech and J. M. Rodrigues, “A new crypto-watermarking method for medical images safe transfer, ”in Proc. of the 12th European Signal Processing Conference, Vienna, Austria, pp. 1481–1484, Sept 2004.
