Authentication of Medical Images using Integer Wavelet ... - IJETAE

International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 9, September 2012)

Authentication of Medical Images using Integer Wavelet Transforms A.Kannammal1, Dr.S.Subha Rani2 1

Assistant professor, 2Professor and Head, ECE Department, PSG College of Technology, Peelamedu, Coimbatore With this reason, preventing medical image from forgery becomes very important, and a digital watermarking technique can be a solution in resolving such a problem. This shows that the demand for medical image authentication have become enormous.

Abstract - In these days, medical image exchanges between hospitals have become a vital technique as a result of the development of the latest technology in communications and computer networks. A new fragile watermarking algorithm for medical images using selective bit plane of integer wavelet transform domain is proposed and implemented. This algorithm makes it possible to resolve the security and forgery problem of the medical images. For the selection of insertion planes, n random numbers are generated, which have the integer value from 1 to 8, and the required plane is selected using the key.MSB Values are given as input to obtain the hash value. The output of the hash function is embedded into the selected plane, and it is combined with the MSBs to get the watermarked coefficients. The experimental results show a very good improvement in image quality in terms of PSNR compared to Reversible watermarking technique using Hash algorithm. This Algorithm shows an excellent Tamper detection capability and shows the exact location of the tamper. In addition, by using integer wavelet transform, this algorithm can utilize Hash function and improves the security of the inserted watermark.

II. METHODOLOGY A new fragile watermarking algorithm for medical images is proposed. This algorithm makes it possible to resolve the security and forgery problem of the medical images. Instead of the discrete wavelet transform, an integer wavelet transform is used to utilize hash function. The watermark associated with the hash values is inserted into the LSBs of the integer wavelet transform coefficients. This algorithm also detects a forged area of the image very well. The conventional fragile watermarking methods have handled a watermark for image; Most of them insert the watermark into the LSBs of cover image. In this case, they have a problem that the watermark can be removed easily by modifying LSBs. To overcome such a problem, a new fragile watermarking algorithm using selective bit plane mechanism is proposed .The proposed algorithm uses bit plane schema in integer wavelet transform domain and solves the LSB inserting problem, which is used in conventional fragile watermarking techniques. In this technique, the inserted bit plane is selected randomly with a key. It also utilizes integer wavelet transform domain instead of spatial one. One advantage of using an integer wavelet transform is that the transformed coefficients are integers, which can be used in a hash function. An integer wavelet transform is used in this algorithm instead of discrete wavelet transform because the Hash function can encode integer values only. The coefficients of discrete wavelet transform are not integers, and the coefficients have to be changed into integers in order to utilize hash function. Making the coefficients integers by converting the real values may cause error when the inverse transform is performed. On the other way, the coefficients of integer discrete wavelet transform are integers, and these coefficients can be used in hash function encoding directly without any problems. This also makes it easier in getting a digital signature.

Keywords - Discrete wavelet Transforms, DICOM, fragile watermark, PSNR

I. INTRODUCTION Biomedical image security is an important field of research in medical informatics, which is continuously growing. The increasing adoption of information systems in healthcare has led to a scenario where patient information security is more and more being regarded as a critical issue. Allowing patient information to be in jeopardy may lead to irreparable damage, physically, morally, and socially to the patient, potentially shaking the credibility of the healthcare institution. Medical images play a crucial role in such context, given their importance in diagnosis, treatment, and research. Therefore, it is vital to take measures in order to prevent tampering and determine their provenance. This demands adoption of security mechanisms to assure information integrity and authenticity. However, the security problems have emerged seriously in network transferred medical image Tampering medical image may cause serious results in medical treatments. 104

International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 9, September 2012) 2.1. Fragile Watermarking It is important to detect and localize even the slight changes of an image for authentication. In this case, the watermarks must be destroyed easily by most of attacks. These kinds of watermarks are known as fragile watermarks. The fragile watermark should have the following properties: (1) Detection of tampering: It is the most fundamental characteristic of a fragile watermark. It should detect any modification or forgery in a watermarked image. (2) Perceptual transparency: It should not visible under normal observation. (3) No requirement of the original image: It has to extract the watermark from the watermarked image without the original one. (4) Detection of location: It means the capability of locate the changed regions within the watermarked image. 2.2 . Watermark insertion 1. The medical image is transformed into the timefrequency domain using IWT 2. Low -low (LL) band is chosen after the first-level IWT of an input cover image. 3. For the selection of insertion planes, n random numbers are generated, which have the integer value from 0 to k, and select plane using the key. 4. Input MSB values into a hash function and get hash value. 5. The output of the hash function is embedded into the selected plane, and it is combined with the MSBs to get the watermarked coefficients. Inverse integer wavelet transform is done to get the watermarked image LL Band

Select Plane

Hash Function Plane Extract MSB Values

3. Input MSB values into a hash function and get hash value. 4. Compare extracted LSBs with hash value. 5. If the integer wavelet coefficients are changed by any attacks, the hash value will be changed. 6. This change makes it possible to detect any changes of the corresponding coefficients and the corresponding locations in the spatial domain. Watermarked image

LL Band

Select Plane

Key

Hash Function Plane

Compare

Extract MSB Values

Watermarked Extracted image

Extract LSB Values

Fig 2. IWT - Watermark extraction process

III. RESULTS AND DISCUSSIONS The test images used are 256X256 sized JPEG and DICOM images. The test samples used are shown in fig3.Medical image authentication using integer wavelet transform and hash function and the results are shown below

Embedding

Watermarked image

Key Fig .1. IWT - Watermark embedding process

2.3. Watermark extraction 1. The watermarked medical image is integer wavelet transformed as did in the insertion procedures. 2. Select bit plane of each block using the key.

Fig .4 Results for CT-MONO2-16-Ankle (DICOM)

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International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 9, September 2012)

IWT

7 6 5 4 3 2 1 0

MSE PSNR/10 Time elapsed

Fig 7. Comparison graph for IWT

3.1 Discussion for IWT algorithm The test images used in this algorithm were three 256X256 sized JPEG images and three 256X256 sized DICOM images. This algorithm is invertible and it supports all data formats such as JPEG, DICOM, TIFF, GIF, etc. The simulation platform used was MATLAB R2007b. This algorithm also has excellent tamper detection capability and it shows the exact location of the tamper. As the watermark is embedded in a selective bit plane of the MSBs, this algorithm improves the security of the watermark to a greater extent. The simulation results are shown in table 2. The PSNR values are equally good for both JPEG and DICOM images. The time elapsed is also very low for all the images. This can be observed clearly from the graph shown in fig7. 3.2. Comparison of performance metrics of RWA, ICA and IWT based algorithms

Fig 5 Results for Brain-MRI (JPEG)

Fig 6.Results for Transverse Brain (JPEG) Table 1 Comparison of performance metrics for IWT Para meter s

CTBrain

Brain -MRI

Tran svers e brain

Ankl e

CTBrai n

Brai nCS

Lung s

MSE

0.074

0.0640

0.0805

0.0547

0.074

0.061

0.062

60.747

59.40 9

60.26 3

Lungs

10 8 6 4 2 0 RWA

IWT

ICA

MSE PSNR/10 Time elapsed

PSN R (dB) Time elaps ed (s)

59.409

60.068

59.074

60.190

RWA- Reversible watermarking using Hash algorithm. IWT-Integer wavelet and Hash algorithm. ICA-Independent component analysis. 4.176

3.615

3.562

4.156

4.176

4.260

3.653

Fig. 8. Comparison graph of three algorithms for LUNGS

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International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 9, September 2012) Table2 Overall comparisons of performance metrics Sl .N o

Wavele t used

Test samples

1

Lungs

2

Brain MRI Discrete Wavelet

(dB)

Time elaps ed(s)

3.1633

43.1293

3.211

4.0713

42.0355

3.309

PSNR Type

JPEG JPEG

MSE

Transverse Brain

JPEG

3.5656

42.6095

3.443

CT-MONO216-ankle

DICOM

0.0021

32.896

3.913

5

CT-MONO216-brain

DICOM

0.0043

30.345

3.7402

6

Brain CS

DICOM

0.0023

32.563

3.9753

7

Lungs

JPEG

0.0130

67.0014

9.885

8

Brain MRI

JPEG

0.0015

76.3393

10.077

Transverse Brain

JPEG

0.0163

66.0099

10.054

CT-MONO216-ankle

DICOM

0.0843

57.987

3.548

11

CT-MONO216-brain

DICOM

0.0854

56.8723

3.143

12

Brain CS

DICOM

0.08345

52.981

3.432

13

Lungs

JPEG

0.0622

60.1901

3.653

14

Brain MRI

JPEG

0.0640

60.0683

3.615

15

Transverse Brain

JPEG

0.0805

59.0742

3.562

CT-MONO216-ankle

DICOM

0.0547

60.7471

4.156

17

CT-MONO216-brain

DICOM

0.0745

59.4092

4.176

18

Brain CS

DICOM

0.0612

60.2632

4.260

3

4

On comparing the performance metrics of the three different algorithms, it is found that PSNR values for ICA and IWT algorithms are far better when compared to the Reversible watermarking algorithm .i.e., they show better improvements in image quality in terms of PSNR with an increase of about 15 to 20dB than the Reversible watermarking algorithm. In terms of computation time, the ICA based technique takes a longer time. This is mainly due to the algorithms used to compute independent components. This can be inferred by comparing the time elapsed with the other algorithms. The overall simulation results of the three algorithms were tabulated in table 6.4. In Reversible watermarking using Hash algorithm, when DICOM images are used, watermarked images of very poor quality were obtained. This algorithm did not support all the data formats. The average PSNR of this algorithm comes around 42 dB. In ICA algorithm, the time taken for computation is very large. When DICOM images are read, watermarked images showed a very poor quality. The average PSNR of this algorithm comes around 69 dB. In IWT based algorithm, the average PSNR is close to 60 dB. It effectively processes the DICOM images. Yet another important advantage of IWT is that, when an image is transformed into a wavelet domain using DWT, the values of the wavelet coefficients will be the floating point. If these coefficients are changed during the watermark embedding, the corresponding watermarked image block will not have accurate values. Any truncation of the floating point values of the pixels may result in loss of information and may ultimately lead to the failure of the reversible authentication watermarking systems, that is, the original image cannot be exactly reconstructed from the watermarked image. Information may be lost through forward and inverse transforms. Furthermore, conventional wavelet transform is in practice implemented as a floating point transform followed by a truncation or rounding since it is impossible to represent transform coefficients in their full accuracy. Hence information is potentially lost through forward and inverse transforms. To avoid this problem, an invertible integer to integer wavelet transform based on lifting is used here. It maps integers to integers and does not cause any loss of information through forward and inverse transforms.

(RWA)

9

Discrete Wavelet

10 (ICA)

16

Integer Wavelet

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International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 9, September 2012) [5]

IV. CONCLUSION Three different algorithms(Reversible watermarking using Hash algorithm, Content based watermarking using independent component analysis, Medical image authentication using Integer wavelet transform and Hash algorithm) have been analysed for authentication of medical images. In medical imaging, picture archiving and communication systems (PACS) are computers, commonly servers, dedicated to the storage, retrieval, distribution and presentation of images. The medical images are stored in an independent format. The most common format used in hospitals for image storage is DICOM (Digital Imaging and Communications in Medicine).From the comparative studies, it has been found that Integer wavelet transform and Hash algorithm is most suitable for the authentication of DICOM images. The PSNR obtained using this algorithm is close to 60 dB and shows better improvements in image quality. By using integer wavelet transform, this algorithm can utilize Hash function and it improves the security of the inserted watermark. Both ICA and IWT shows excellent tamper detection by locating the exact position of the tamper. In terms of computation time, the ICA based technique takes a longer time, whereas IWT based algorithm has a little elapsed time.

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