Proceedings of the International Conference on Computer and Communication Engineering 2008 May 13-15, 2008 Kuala Lumpur, Malaysia

978-1-4244-1692-9/08/$25.00 ©2008 IEEE

A Reversible Watermarking with Low Warping: An Application to Digital Fundus Image

Poonkuntran Shanmugam, Rajesh .R.S, Eswaran Perumal

Department of Computer Science and Engineering Manonmaniam Sundaranar University, Tirunelveli-627 012, India

Email: s_poonkuntran@yahoo.co.in, rs_rajesh1@yahoo.co.in, eswaranperumal@yahoo.co.in

Abstract

Medical image security is an essential in the present world which is desired in transferring the medical images in inter or intra hospital network for diagnosis. The medical images are differed from ordinary images. Because, they are captured through different devices (for example X ray, Computed Tomography, Magnetic Resonance Imaging and Positron Emission Tomography) and different color filters (for example Red, Green and Blue). This paper describes a new reversible watermarking scheme with low alterations for digital fundus images. It is based on the special nature of fundus images in its color channels. The proposed scheme in this paper is invariant to insertion process which removes the alterations from the images once the watermark is identified. It identifies the pixel optimally for inserting the secret message in it by reviewing the capability of pixel for carrying the information and the impact of the insertion process. This process is maintained by the variable α – alpha which is newly introduced here to maintain the optimality. In the experiment conducted on test fundus images (The test images were taken from DRIVE and STARE public databases), the proposed scheme gives better PSNR value which is 78.7222dB for red channel and 81.0685 dB for blue channel at the capacity rate of 0.1250 bits per pixel (bpp). It is also preserving the quality of images in good condition for diagnosis since the green channel of the image is untouched.

Keywords: Fundus image, image security, medical Imaging, reversible watermarking.

I. INTRODUCTION The security of medical images is obtained from

strict ethics and legislative rules which can be

classified in three fixed characteristics: confidentiality, reliability and availability [1] [2]. Confidentiality: It means that only the entitled users have access to the images in the scheduled system. Reliability: It is given by two features. i) Integrity: Ensuring that the images have not been modified by unauthorized person. ii) Authentication: Ensuring that the image belongs indeed to the correct patient and is issued from the correct source. Availability: It is the capability of an image to be used by the entitled users in the normal scheduled conditions of access and exercise.

Providing security for medical imaging is very important, when the images are exchanged in hospital networks. In such case, the first two characteristics have mainly to be considered. The watermarking scheme has been recognized to control the image reliability by emphasizing its integrity and its authenticity [1]. In general, watermarking modifies or modulates the pixels gray level of an image, to embed or insert a message in it. In the image reliability, a proof of the image like digital signature and IIN (Image Identification Number specified by the DICOM standard [4]) are embedded in the image. Other features of images can also be embedded by adding meta-data over the pixels without altering the image format. A digital watermark is a secret key dependant signal inserted into digital data (images, sound, and texts) and which can be later detected/extracted in order to make an assertion. The location of watermark in the image determines two kinds of methods: The spatial domain methods which embed watermark information directly into images pixels. The frequency domain methods which embed watermark information in the transform domain. In medical images, alterations due to the insertion process are not accepted by physicians for diagnosis [3]. Requirements in medical images are differed from multimedia applications [3]

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[5] [6]. A watermarking scheme is defined by three properties: Capacity: It refers to the number of bits per pixel (bpp) that can be used to embed the information within it. Robustness: It refers to the ability of the embedded message to overcome the insertion problem such as alteration in the pixels and information loss. Invisibility: It refers to the no external identification mark is in the image. Identification information is hidden in the image.

Though few studies have been conducted in the area of medical imaging security, the three possible solutions have been identified in [1]. The first solution is embedding the watermark in Regions of Non-Interest [5]. In most cases, these regions correspond to the black areas of the image. For example, In CT scan images, the background regions are used for embedding watermark. In this way, the watermark will not interfere with the information in foreground regions considered during the diagnosis. The second solution is based on reversible watermarking. Once the watermark has been detected or read, the reversibility property guarantees that the watermark can be completely removed from the image, allowing retrieving the original image pixel values [6] [7] [8] [9] [10] [11] [12] [13]. The third solution is based on non-reversible watermarking method that will introduce sensible modification as in lossy image compression. However in such case, there is a proof which ensures that the watermark will not introduce any doubt during image interpretation has to be assessed.

In this paper, we propose a reversible watermarking scheme for digital fundus images based on generic approach by keeping the condition that inserted watermark will not have any impact in the diagnosis.

The paper has been organized as follows. Section I gives the introduction to watermarking in medical imaging. Section II presents about digital fundus images. The proposed water marking scheme is given in Section III. Section IV shows the experimental results conducted on digital fundus images. Section V gives conclusion and it is followed by future enhancement in section VI.

II. ABOUT DIGITAL FUNDUS IMAGES Ophthalmology is the branch of medicine which

deals with the diseases and surgery of the visual pathways, including the eye, brain and areas surrounding the eye, such as the eyelids. In ophthalmology, the fundus is the interior surface of the

eye, including the retina, optic disc and macula. The fundus can be viewed with an ophthalmoscope. The fundus images are taken using fundus camera. A fundus camera is a specialized low power microscope with an attached camera designed to photograph the interior surface of the eye. The example of fundus images is shown in Figure 1. Fundus photograph is usually taken using a green filter to acquire images of retinal blood vessels. Green light is absorbed by blood and appeared darker color in the fundus photograph than the background and the retinal nerve fiber layer. Hence, the green channel of the fundus images posses the valuable information for diagnosis than other channels [3]. This was the key point which motivated us to develop a reversible watermarking scheme for digital fundus images using only red and blue channels of the images.

Figure 1. Digital Fundus Image.

III. A REVERSIBLE WATER MARKING SCHEME It is found that few works have been carried out in

security of medical images using watermarking. Development of watermarking scheme for medical images using its special properties and natures gives better results than one which uses conventional methods[2] [3].

The watermarking methods are classified into two categories. The first category of methods applies encoding schemes on the features of an image [7] [8] [9]. In such methods, the original information is encrypted, before embedding it in to an image. These methods have high watermarking capacity which has mainly been devoted to protect the image integrity due to their fragility. However, the distortion increases along with capacity, the performance based on the algorithm used for cryptography. The second category of methods is considered as non-memory techniques [10] [11] [12] [13]. In these methods, the watermark is embedded in the image based on additive aspect. It is applied in the limited and possible depth of the image (0 to 2p-1 possible gray values of an image whose pixels represented in P bits). It requires a method for taking care of under flow (Negative Pixel Values) and over flow (Exceeding the possible limit of the pixel).

Arithmetic modulo [10] [11] and functions with cyclic properties [12] are few approaches have already

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been proposed in the direction of the proposed scheme. When, arithmetic modulo is used, it introduced salt and pepper noise in the image which have been eliminated by applying cyclic properties based function proposed in [12]. Shifting histogram ranges is another method in the same direction. It applies the principle called Peak point to Zero Point. It shifts the ranges of pixel values from its peak point to zero point where one pixel is left to embed the information. Here, the impact of the insertion process was less compared to previous approaches.

The proposed scheme is in the second category of methods. The method is based on histogram which looks into identify the optimal gray value of concerned channel of the fundus image to embed the information instead shifting the ranges of values from peak to zero point. Let P be the set of possible and available pixels in an image.

P= {Pi}, i = 1 to n. (1)

Where n are number of available pixels in an image. The optimal pixel in the set P is identified by two steps. The first step checks that whether the Pi is capable of carrying the information or not. It refers the compatibility between frequency of the Pi in the image and number of bits of information is to be embedded. The second infers the impact of capable Pi identified in the first step, if insertion of information is carried out in that Pi. The information is embedded in optimal Pi’s least significant bits (LSB).Therefore insertion process is identifying the suitable pixel (Pi) from the pixel set (P) to embed an information bit b within one pixel set that will be correctly decoded. This insertion process creates two situations. In one, it will produce changes in LSB of Pi. In such case, the watermarked pixel is given by

Pw = f (Pi, b). (2)

In the next, Pi is modified without introducing binary information.

Pw = f (Pi, Ф). (3)

Where f is modulating function which adds or subtracts gray level of pixel. The proposed reversible insertion process is shown in Figure 2.

Consider an image of size MxNxK, the insertion process works as follows.

Step 1: Read the image and extract the required channel for insertion (Red and Blue channels are used in our approach).

Step 2: Sub divide the channel images into blocks and ordered in secret manner. This will be used as key and known at decoding stage.

Step 3: Prepare the pixel set (P) of the channel images. Calculate the frequency of pixels in the

channel. Step 4: Compatibility Check for pixels.

The difference may not be the same for all cases. In many cases, frequency of pixels will be larger than size of the information. To control this difference, we introduced a new parameter called difference ratio α (alpha) which can be calculated as follows. α = (Freq (Pi) – Size (Info) / Freq (Pi)) * 100 (4)

Where Freq-frequency of the pixel, Size-size of the information and Info-information is to be embedded. The minimum and acceptable value of alpha (0% to 25%) is chosen in the process based on the availability Pi.

Figure 2. Insertion Process of one bit b in Optimal Pixel Pi in

reversible manner. Step 5: Impact Check for pixel. For selected Pi, the forward neighbor pixel is

examined for making Pi as watermarked pixel. The Pi is finalized with minimum neighbor’s frequency. The neighbor pixel is shifted to its neighbor for making insertion process in reversible manner. In this way, the Pi is selected as an optimal pixel for insertion process by satisfying the both steps 4 and 5.

Step 6: If Info (i) = = 1 then,

Pw = Pi + 1 (5)

Pi

Pw = f (Pi, Ф)

Check Next Pixel in P

Pw = f (Pi, b)

No

Yes

No

Capable of carrying

information

Any Impact on Insertion

Process

Yes

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If Info (i) == 0 then

Pw = Pi (6)

Step 7: Select any one pixel as key where Pi is embedded. This will also be known along with ordering of block in the channel images at the decoding stage.

At the decoder stage, watermark decoder works in the same way. Using secret key, the blocks are reordered and Pi is identified. Then, the water mark is extracted by following conditions.

If the pixel value is Pi, then Pr(i) = 0 (7)

If the pixel value is Pi+1, then

Pr(i) =1. (8)

Where Pr is extracted watermark. By combining the Pr in linear fashion, the original water mark is extracted.

IV. RESULTS The proposed watermarking scheme has been

simulated in MATLAB 6.5 using around 150 digital color fundus images. These images were taken from DRIVE and STARE public databases [3]. The images in the databases were in different formats. We brought it to the fixed size of 256x256x3, 8 bits per pixel in color channel and represented in TIF format.

Figure 3. Information embedded in the image

Different information can be embedded within

images of this database. Seven types of information as shown in Figure 3 were embedded in the images. It consisting of 64 bits IIN (Image Identification Number [5]), 160 bits digital signature [4], 48 bits manifest type that includes codes for various types of alterations in the retina (for example, emboli refers to emboli manifestation) and 128 bits vessel information that includes optic disc – cup to disc ratio(C/D) and vessel width details. Finally the total information came around 400 bits. This information is segmented into fixed size of binary sequences which has been embedded in optimal gray value of the color channel images to provide high reliability of the images. It has been noticed that the proposed watermarking scheme

does not have any visual difference; almost they are identical as in Figure 4. To evaluate the proposed scheme quantitatively, we used two parameters Capacity rate(C) in bits per pixel(bpp) and peak signal to noise ratio(PSNR) in decibel (dB) between original image(I) and its watermarked version image(Iw) which have been found as best parameters in measuring the fidelity of the method [2].

C= Cnt_Emd_Bits (I) / Cnt_Pixels (I) (9)

PSNR (I, Iw) = 10 Log10 [(2p-1)2/ MSE] (10)

Where Cnt_Emd_Bits ( ) is number of embedded bits in the image, Cnt_Pixels (I) is number of pixels in the image I, p is image depth (8 bits in the proposed scheme) and MSE is mean square error.

a b

Figure 4. a) The original image b) Watermarked image

For the comparative study, the histogram shifting

[6] method has been chosen. It is a basic scheme for watermarking. Moreover, no similar work found in the literature for fundus images, it is chosen for comparison. When the pixels are chosen optimally for embedding information, it modifies the neighbor pixel. MSE will be equal to 1 leading to a PSNR value not smaller than 48.131dB. On a set of 150 images from the public databases; we conducted the experiment as follows. The information in the size of 400 bits is spitted into 4 equal sized (each in 100 bits length) sequences. Since, the LSB of the pixels only used in the approach for insertion, capacity rate is at 0.1250 which can be varied based on the number of bits used for embedding. In our approach, capacity rate retained at 0.1250 because of two color channels used for embedding 400 bits of information.

We inserted two sequences in each color channels except green channel which is not modified. The PSNR values of red channels in proposed scheme varied from 77.7223 dB to 81.6716dB and blue channels in the proposed scheme varied from 76.8838 dB to 84.939 dB. The average PSNR value of red channel is around 78.7222dB and blue channel is around 81.0685 dB. When the experiment is conducted on histogram shifting method, it gave the 68.5891 dB

CAY34567 // IIN SHANMUGAMPOONKUNTRAN // Digital Signature EMBOLI // Type of Manifest 0.45 // C/D ratio - Optic Disc 0.33 // Maximum Vessel Width 0.22 // Median Vessel Width 0.44 // Minimum Vessel Width

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average PSNR for red channel and 68.2187 dB for blue channels.

In the comparative analysis, it is found that the proposed scheme improves the PSNR of images at the rate 23.2506% for red channels and 24.7877% for blue channels.

S1 S2 S3

S4 S5 S6

S7 S8 S9

S10 S11 S12

Sample Images

Histogram Shifting Proposed Scheme

PSNR at Red(in

dB)

PSNR at Blue(in

dB)

PSNR at Red(in

dB)

PSNR at Blue(in

dB) S1 65.6274 62.7931 77.9071 82.3162 S2 67.6624 65.2066 78.3022 81.6716 S3 86.0153 89.6055 78.0349 80.275 S4 65.6673 62.2189 79.3936 82.6158 S5 64.5463 60.4204 78.0133 80.4978 S6 65.7699 64.7585 79.0802 81.7218 S7 72.8912 92.6158 79.8941 81.6716 S8 54.6758 62.3575 77.8035 84.939 S9 63.4191 65.3509 77.8446 78.9449 S10 53.7794 62.6727 81.6716 76.8838 S11 63.6215 65.1696 78.9986 81.2896 S12 63.3938 65.4546 77.7223 79.9947

Note: All values are at the capacity rate of 0.1250 bpp.

Figure 5. Sample images and its PSNR values

Thus, the image quality is preserved at good

condition, because of no modification done in green channel and better PSNR values of other color channels. The Database contains the images in 50

disease classes. In our experiment, 150 sample images were taken from these 50 classes. On these, some of the sample images and corresponding PSNR values for both histogram shifting and proposed scheme are shown in Figure 5.

From the experiment, it is also noticed that the proposed watermarking scheme maintains stable PSNR values at the level of around 80 dB approximately for both red and blue color channels. The optimality process in the proposed scheme brings the stability in PSNR of images. Because, it selects the compatible Pi with minimum number of neighbors rather selects the compatible Pi. The improvements of PSNR in the proposed scheme are shown in Figure 6.

Percentage of PSNR Improved in Proposed Scheme

0

10

20

30

40

50

60

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12

Sample Images

Perc

enta

ge o

f PSN

R

Red Channel Blue Channel

Figure 6. Illustration of PSNR values of samples

V. CONCLUSION The proposed scheme is invariant to insertion

process which identifies the optimal gray value for insertion along with the review of the impact of insertion process. Experiments shown that the proposed scheme gives better PSNR values (80dB approximately for Red and Blue Color Channels) and there is no visual difference in the images. Hence, the quality of images is preserved in good condition for diagnosis.

The paper discussed a new reversible watermarking scheme for digital color fundus images. It is based on the color channel information of the fundus images. It can also be used for any color images, if any one or more than one color channel of the images is having less important details.

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VI. FUTURE ENHANCEMENT The proposed scheme is first level of security of

medical images. Therefore, it can be extended to providing security for other medical imaging modalities (X-Ray, CT, MRI, SPECT and PET) using their special properties in nature.

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