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Medical Image Watermarking with ANN in Wavelet Domain P.V.V.Kishore, MIEEE, K.Sai Prajwal, M.Kamal Mohan, S.Koteswarao, MIEEE K.L.University, Department of Electronics and Communications Engineering
[email protected],
[email protected],
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The broken white patches at the bottom of the CT image in figure 1(a) point to tumor formed by blood flow block through the abdomen. This diseased patient CT image is from a scan center in a remote location in Guntur, Andhra Pradesh, India. The radiologist identified the problem but to clear his perspective and start a treatment he transferred the CT image to an expert doctor in a city based hospital in Chennai, India. During transit some hacker changes the contents of the CT image as identified clearly in figure 1(b). This illegal change shall prove risky to the patient as well as medical practitioners. Therefore safeguarding medical image contents is a high priority task when transiting through the unsecured networks. Medical image watermarking [1] plays an important role in relieving the patients all over the world by transferring medical images of patients through unsecured networks such as Worldwide Web. Watermarking of medical images provides secure transmission of medical images of patients to doctors around the globe for detailed analysis of their illnesses. This practice helps to expand the horizon of remotely stationed patients to a place where no proper medical doctor is available to increase their chances of survival. Trading medical images through unsecured networks is prone to undesirable change to stuff the images. Medical images contain susceptible information about the life of a human being. Therefore, authentication of medical images such as Ultrasound scans, MRI scans, x-ray and Computer Tomography (CT) scans need watermarking. The host medical image watermarked with patient information before transferring to ensure security. Receiving doctor strips the watermark before continuing for diagnostics. A digital watermark is a visible or invisible detection code that hides inside a medical image. Medical image authentication calls for a nonvisible hidden watermark [2]. Medical image watermarking plays a prominent role in telemedicine applications [3]. Digital watermarks for medical images are patient information that is unique to a particular patient [4]. A Watermark extraction proves the authenticity that these medical images belong to a particular patient [5]. Watermarking extensively used to protect every known digital media contents like text documents [6], images [7], audio and video data. Over the past decades the watermarking methods concentrated on image’s spatial domain [8], transform domain [9], robust [10], semi-fragile [11] and fragile [12]. The last three methods protect the watermark against malicious attacks while transporting images on unsecured networks. The watermarking techniques should satisfy three basic needs for successful watermarking which are robust, imperceptibility and competence. This research proposes to use wavelet transform [13]-[16] and artificial neural network back-propagation algorithm for
Abstract—Medical images hold health information about a patient. Due to their inability to show information clearly and lack of expert doctors, motivate patients to send their imaging reports using unsecured Internet. The objective is to provide security to medical images of patients passing through unsecured networks through watermarking methods. The watermarking will most likely will alter pixels in the medical image reducing object composition of the medical image. Medical image quality affects the health of a patient. To avoid such quality issues during watermarking, the algorithm uses artificial neural network (ANN). ANN remembers and rebuilds the degraded pixel information by its location during watermarking and transmission. Embedding and extraction of watermark uses wavelet domain. Experimentation performed on three types of medical images Ultrasound imagery (US), Magnetic Resonance Imaging (MRI) and Computer Tomography (CT) of test subjects brought from hospitals in Vijayawada in India. The psnr and normalized cross correlation coefficient (ncc) measures the visual quality and quantity of the watermarking procedure with ANN. The DWTANN based method preserves the quality of the medical images. Index Terms—Medical Image Watermarking, Discrete Wavelet Transform, Watermark Patient image, Artificial Neural Network, Medical Image reconstruction, MRI, CT and US Medical Images.
I. INTRODUCTION
W
has revolutionized the way digital media travels on unsecured networks such as the internet. It helps in identifying and recovering (if attacked) data theft of multimedia information traversing the internet. The coming of civilization in the past decade saw a tenfold increase in diseases affecting human race. But the increase in expert doctors is below the affected human race. Therefore, expert doctor consultations were possible through transfer of medical images through the internet. Using unsecured internet may sometimes damage the image contents making it tough for the doctor to detect the disease risking patient’s life. Figure 1 shows how hackers change a CT medical image with tumor. ATERMARKING
Fig.1(a). CT abdomen with Tumor , (b). CT abdomen with Tumor modified by Hacker
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medical image watermarking. The watermark is a patient image embedded into the medical image with a KEY. Here KEY is a position matrix containing locations in the medical image with the watermark. The neural network training identifies a relation between 3×3 block pixels. This helps to rebuild the medical cover image after watermark extraction. The patient image as watermark embeds into to the medical image using 2D DWT. But seeing the scale of wavelets, performance testing of different mother wavelets for embedding resulted in choosing the best mother wavelet for watermarking. Also various decomposition levels of wavelets helped us reach a conclusion on mother wavelet and it's level. Finally the watermark extraction involves applying KEY position matrix and trained neural network. Remaining paper organizing is as follows. Section 2 deals with the watermarking embedding algorithm and extraction algorithm. Section 3 introduces algorithm performance parameters for reviewing the watermarking results. The discussions present insight into the use of wavelet transform with artificial neural networks for medical image watermarking. Finally conclusions on the medical image watermarking procedures embrace section 4.
arbitrary starting scale and the coefficients Wϕ define an approximation of f at scale j0 . Wϕi ( j0 , m, n) =
ϕ j , m ,n and ψ ij ,m, n mark scaled and translated basis functions as shown below,
ϕ j ,m,n ( x, y ) = 2 j / 2 ϕ (2i x − m, 2 j n − 1)
(3) ψ i j ,m,n ( x, y ) = 2 j / 2ψ i (2i x − m, 2 j n − 1) Given Wϕ and WΨ , I Mi ( x, y ) is inverse DWT formed by the expression: M −1 N −1 ⎛ ∞ (4) ⎞ 1 j W i⎟ ⎜ W j0 ϕ j + I Mi W = iW j 0 ψ MN
A. Discrete wavelet Transform The 2D medical image I Mi ( x, y ) passes through a low pass filter and a down-sampler of level 2 to produce an approximate image at level-1 wavelet decomposition.
Similarly 2D medical image I Mi ( x, y ) passes through a high pass filter and down-sampler to create detailed image at level-1 wavelet decomposition. Further in level2 decomposition the low frequency information divides into approximate A(L-2) and detailed D(L-2) components. In the nth level decomposition the approximate low frequency component in (n-1)th is decomposed into approximate low frequency information A(L-n) and detailed high frequency D(L-n). The notion L2(R2), where R is a set of real numbers, stand for the finite energy function of I Mi ( x, y ) in R2; and x,y in
s2D
__________
(
Mi
( x , y )ϕ ( j0 , m , n )
n
⎟ ⎠
j = j0
⎡⎛ __________ ⎢⎜ ϕ k x i , y j − ϕ k x i , y j ⎢⎜ j =1 ⎢⎝ ⎣ m
∑∑ i =1
)
(
)
(
)
⎞ ⎟ ⎟ ⎠
2 ⎤1/2
⎥ ⎥ ⎦⎥
(5)
gives the mean value of coefficients. The center
mi
ci
matrix P ( x , y watermark image.
The functions ϕ H ( x, y ) , ϕ V ( x, y ) and ϕ D ( x, y ) represent gray level variations along different directions such as horizontal variations, vertical variations and diagonal variations. The 2D DWT of I Mi ( x, y ) having size M × N is
∑∑I
i
position of each 3×3 block gets extracted after arranging the standard deviations in descending order. Position
V
ϕ D ( x, y ) produced in figure 2.
MN
1 = nm
ϕ k xi , y j
and three two dimensional wavelets ϕ ( x, y ) , ϕ ( x, y ) and
M −1 N −1
∑∑
Where ϕ k ( x, y ) are wavelet coefficients for k=H, V and D, high frequency components of wavelet transform.
R. In a 2D wavelet transform, a 2D scaling function ϕ ( x, y ) ,
1
∑ ∑⎜
m = 0 n =0 ⎝
B. Block based position matrix-KEY The medical cover image transforms into the wavelet domain for embedding the watermark patient image. Next select the wavelet coefficients and their locations as a position matrix. Watermark pixels locations in cover image defined by position matrix without damaging the contents of the medical cover image. Divide medical image wavelet coefficients, ϕ H ( x, y ) , ϕ V ( x, y ) and ϕ D ( x, y ) into 3×3 blocks. Estimate the standard deviation of each block and arrange the standard deviations in descending order. Choose ‘n×m’ standard deviation blocks matching the watermark size. Extract the block’s center which points to the watermark embedding position. This position matrix acts as a KEY during extraction. The saved position matrix travels along with the watermarked image to the destination network. The 2D standard deviation of wavelet detail coefficients ϕ k ( x, y ) computes as
This method of watermarking explores ANN for embedding and extraction of watermarks from medical images in wavelet domain. The watermark used is a patient image and the watermarked image is a medical cover image. The watermark KEY is a position matrix formed from the block based standard deviation values from the cover image.
Wϕ ( j 0 , m , n ) =
∑∑
(2) I Mi ( x, y )ψ ij ,m, n MN x =0 y = 0 The coefficients in equation add horizontal, vertical and diagonal details shown in fig.2 for scales j ≥ j0 . The
II. MEDICAL IMAGE WATERMARK EMBEDDING
H
M −1 N −1
1
ci
) acts as KEY for extracting the
C. Error back-propagation Neural Network for Watermarking
The wavelet coefficients divide into 3×3 blocks each as shown in the fig. 2. There is a high non linearity between pixels in the 3×3 block shown in fig.2. This relational nonlinearity between pixels is preserved by exploiting the nonlinearity of neural networks. Neural networks repeat
(1)
m =0 n =0
Where j, m, n, M, N are integers, i= {H, V, D}, j0 an
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nonlinear functions performed by a human brain. Here neural network correlates the target pixel in each block ϕmH,n ( x, y ) with their eight neighboring pixels
matrix. S4. The watermark is embedded using the formula
ϕmH± l ,n ± l ( x, y ) l = [−1, +1] as in figure 2. From figure 2, the
where W k (i, j ) is watermarked medical image with patient
target pixel ϕ mH, n is where the watermark pixels of the patient
image I pw ( xi , y j ) . ϕ k ( xi , y j ) is nth level wavelet sub-
W k (i, j ) = ϕ k ( xi , y j ) + ( 2σ + δ ) (2I pw ( xi , y j ) − 1)
(6)
bands of medical image. σ is the ratio of standard deviation of wavelet coefficient block and the maximum standard deviation of all the coefficient blocks. δ is the fixed embedding watermark strength which is fixed at 0.05 in this paper. I pw ( xi , y j ) is the encrypted patient image at ith and jth positions. S5. Finally, assemble all the modified sub-bands and apply inverse 2D Wavelet Transform (IDWT) and is formulated as
image get embedded. The target pixel of each block may get a pixel from the patient image depending on the standard deviation of the block.
(
I WMi = W ( n )
)
−1
(7)
where ‘n’ represents 4 sub-bands for n=1, LL, LH, HL, HH is the watermarked medical image. The watermarked medical image I WMi is obtained which contains the patient image. This watermarked medical image is transmitted to unsecured networks to servers of major hospitals around the world to expert medical practitioners. E. Watermark Extraction Algorithm The watermarked medical image I WMi is sent distantly through unsecured internet servers to expert medical doctors from remote parts of the world. At the doctor’s place the system decouples the attacked watermarked medical image form the watermark for authentication. The following extraction process is incorporated at the doctor’s side to extract the encrypted watermark patient image and decrypt the patient watermark image.
Fig.2. Block Based Processing of High Frequency Wavelet Coefficients
The remaining eight pixels from each block forms the input vector for the neural network and the center pixel form each block forms the output coefficients. A neural network object is proposed for this application The neural network used is a feed forward network trained with an error backpropagation algorithm [17]. ANN object contains 8 input neurons from 8 neighboring pixels and one output neuron for one center pixel as target neuron. There are 6 hidden neurons in the network to produce speed of execution and accuracy of the network. The high frequency wavelet coefficients ϕ k ( x, y ) pass through this stage. For each high
S1. The possibly attacked watermark medical image is treated with 2D discrete wavelet transform (DWT) and decomposed to nth level with n sub-bands LL, LH, HL and HH. S2. Medical image is decoupled from the patient watermark image using the inverse expression
frequency coefficient ϕ k ( x, y ) , a feature matrix formed with each column having 8 wavelet coefficients for a particular block. These 8 coefficients are computed using KEY i.e. the position matrix which gives the location of the pixels in a block having least deviation.
I ep ( x, y ) =
2(W RMi (i, j ) − W Mi (i, j )) (2σ + δ ) + 1
(8)
where W RMi (i, j ) is transformed the received watermarked medical image at ith and jth location. W Mi (i, j ) represents the sub-bands of original cover image that is received with the transmitted watermarked image. I ep ( x, y ) gives the recovered watermark patient image. S3. Extracted medical image is then reconstructed with the help of KEY position matrix and by simulating the trained neural network. S4. Finally extracted patient image and Medical cover image are separated.
D. Patient Image Watermark Embedding Algorithm The Medical Images are watermarked with their analogous patient image in wavelet domain which is accomplished using the following steps. S1. Select a 64×64 resolution patient image. For simplicity the image is a gray scale image. S2. Apply 2D filter bank method based dwt on medical cover image with any of the following orthogonal or biorthogonal mother wavelets. In this case it is ‘db2’ mother wavelet. Using this ‘db2’ mother wavelet the cover image is decomposed into up to 2nd level. S3. Apply block based processing on three high frequency components. Extract position matrix having low standard deviation and save that matrix as a KEY position
III. RESULTS AND DISCUSSION The proposed watermarking process is implemented on MATLAB 13.0.1 software with three different types of medical cover images. MRI, CT and Ultrasound medical
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Hence the designed neural network object is having 8 input neurons and one output neuron. Hidden neurons help to reach the target at a faster pace. Hence by using the method proposed in [17], the number of hidden neurons is computed as 8. The wavelet coefficients are divided and reconstructed to form 8×484 pixel sample matrix and the output matrix of size 1×484 pixels.
(US) medical images are used as cover images of standard resolution 256×256. The Watermark is a patient image of resolution 64×64. Since medical images are gray scale images, it is intended to consider gray scale patient image as watermark. The dynamic standard deviation ratio factor σ is used for watermarking in our experiments which is computed from wavelet coefficients. The other scaling factor δ is chosen as 0.05. Here there is no fixed bound for δ as it can be varied within 0.01 to 0.09 for medical image watermarking. The performance of the proposed medical image watermarking is judged by computing peak signal to noise ratio (psnr) and normalized cross correlation coefficient (ncc). These parameters will decide the robustness of the watermarking method using DWT-ANN watermarking process. Watermarking of medical images is relatively vulnerable process as the medical images contain information related to life changing circumstances of human subjects. Corruption of the original medical image using the watermarking process should be within the acceptable confines of human perception. The visual sensitivity of the watermarked and extracted images is mathematically represented by calculating psnr and ncc. The values of normalized cross correlation coefficients (ncc) range from 0 to 1. Larger values of ncc are preferred for better watermarking. A patient’s ultrasound image is first treated with 2D discrete wavelet transform. DWT decomposes the medical image using ‘db2’ mother wavelet to level-1 decomposition. Figure 3(a) shows CT medical cover image (256×256) and Lena image is used as patient image (64×64) in figure 3(b). DWT-ANN watermarking procedure proposed in this paper embeds patient image into an ultrasound medical cover image as shown in figure 3(c) which is a watermarked medical image. Figure 3(d) shows the extracted watermark of patient image.
Fig. 4. Neural Network object used Training and Testing
Fig.5. Regression plot for the ANN under consideration
Training is accomplished using the back-propagation algorithm with a learning factor of 0.01 and with a performance goal of zero. The minimum mean square error is fixed at 0.0001. It took a total of 153 epochs to reach the set goal with a performance measure of 0.00009.
Fig.6. Training Plot of proposed ANN
Fig.3. Proposed DWT-ANN medical image watermarking on CT abdomen images
The regression and performance plots are shown in figures 5 and 6 respectively. Trained network is saved and transmitted along with the watermarked image to the destination servers to reconstruct the medical image. Experimentation was further continued to test the robustness of the algorithm on different kind of medical images which include US and MRI Images.
Figure 3(e) is reconstructed ultrasound medical image with simulated artificial neural network trained using backpropagation algorithm, Visually figure 3 shows that the watermarked image and extracted image match stalwartly as per human visual system. The artificial neural network used for training and simulation is shown in figure 4. Each of the three wavelet coefficients i.e. HL, LH and HH components are divided into 3×3 blocks. The center pixel is the output and the remaining neighboring pixels form the input to the network.
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Six different types of attacks on the watermarked CT images are shown in figure 7. Figure 7(a) gives 3×3 window mean attack, 7(b) median attack, figure 7(c) rotation attack, figure 7(d) noise attack, figure 7(e) shear attack and figure 7(f) shows crop attack on watermarked MRI image with patient data of size 64×64. The extracted watermark after attacks are shown in figure 8. Comparing with watermarked images attacked on our previous algorithm as in [13][14][15 it is observed that, reconstructing the watermarked image using ANN is restraint to attacks in a most promising way.
Figure.9.Visual Comparison of (d) DWT-BAT with three other medical image watermarking methods used by us previously (a) Only DWT (b) RSA-DWT (c) LWT-SVD
Table-1: psnr and ncc for Medical Cover Images Cover Medical Image PSNR(db) NCC MRI 42.8998 0.9965 CT 41.3565 0.9953 Ultrasound(US) 41.3454 0.9942 Fig.7. CT Watermarked Images with ‘db2’ after (a) 3×3 window mean attack,(b) median attack,(c) rotation attack,(d) noise attack, (e) shear attack and (f) shows crop attacks
From the Table-1 psnr in db for MRI, CT and US watermarks are 42.8998db, 41.3565db and 41.3454db respectively. Comparing with psnr values of dwt based watermarking in [13] our proposed DWT on medical images are better and within the prescribed values of watermarking. Normalized Cross Correlation (ncc) coefficient is good for MRI and CT with 0.9965 and 0.9953 compared to US at 0.9942. Again the values are within the permissible range as proposed by DWT-ANN watermarking and compared to results in [13][14][15][16].
Fig.8. Extracted Patient images from CT Watermarked Images with ‘db4’ after (a) 3×3 window mean attack,(b) median attack,(c) rotation attack,(d) noise attack, (e) shear attack and (f) shows crop attacks
Figure 9 shows the comparison of DWT-ANN proposed in this research to other watermarking techniques used by us [13][14][15][16] for medical image watermarking. From figure 9 it can be observed undoubtedly that DWTANN performs distinctively better than other DWT based medical image watermarking proposed by [13]-[15]. This is due to the fact that ANN trained to understand the medical image dynamics of pixels at the time of watermarking will be used as the destination for reconstruction of watermarked medical images. Results are also formulated in Table-I using ncc and psnr for the embedded watermark and original medical image for all three different medical images. The data analysis highlights the usefulness of the DWT-ANN watermarking process for medical image watermarking with patient image as load.
Fig.10. Noise attacked watermarked medical images using (a) DWT based (b) RSA-DWT based (c) LWT-SVD based and (d) DWT-ANN based algorithms
From figure 10 it is clear that DWT-ANN has produced good watermarked medical images than the remaining wavelet based medical image watermarking algorithms
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embedded in the medical image as watermark. At the decoding side the patient image watermark is extracted based on the KEY position matrix. The degraded medical image due to attacks is reconstructed by simulating the saved and transmitted neural network object. Experimentation was done to check the robustness of the algorithm using US, CT and MRI medial images as cover images and lena image as patient watermark image. The results are very encouraging compared to normal watermarking procedures. The proposed DWT-ANN watermarking for medical images demonstrate the ability of this algorithm making it the unquestioned choice towards tele-medicine applications.
under noise attacks. The plot in figure 11 reveals the robustness of DWT-ANN is significantly better than the reaming medical image watermarking algorithms used by us in [13][14][15][16].
REFERENCES Figure.11. Plot showing Extracted ncc values for MRI Medical cover images against various attacks for 4 watermarking algorithms
[1] [2]
Plots of ncc values in figures 12 and 13 show, DWTANN is far superior to other proposed watermarking algorithms even for CT and Ultrasound medical images.
[3] [4]
[5] [6] [7] [8]
Fig..12. Plot showing Extracted ncc values for CT Medical cover images against various attacks for 4 watermarking algorithms
[9] [10] [11] [12] [13] Fig.13. Plot showing Extracted ncc values for US Medical cover images against various attacks for 4 watermarking algorithms
[14]
IV. CONCLUSION A DWT-ANN based medical image watermarking technique is proposed in this research paper. Patient image is used as a watermark. A position vector in the form of KEY is generated using block processing wavelet transformed medical image. On each block standard deviation is applied and blocks with least contributing blocks are ordered in descending order. This gives the positions of least contributing blocks. The center pixels of these blocks form the target vector to the neural network. The input to the neural network is the 8 neighboring pixels of the block. Error back propagation training algorithm is used to train the network. A patient image watermark is
[15]
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[17]
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