Interleaving Scheme for Medical Image Authentication - Springer Link

Interleaving Scheme for Medical Image Authentication Mousami Turuk and Ashwin Dhande

Abstract Recent developments in communication and information technologies have brought new challenges for easier access, storage, processing, and distribution of medical data for health information systems (HIS). In advanced medical applications including telemedicine and telediagnosis there is a need to exchange the information over an insecure network and hence protection of the confidentiality, integrity, and authentication of medical images is a vital issue. Digital watermarking has the potential to contribute important services for data authentication and copyright protection. This paper presents the discrete cosine transform (DCT)based approach for interleaving electronic patient records (EPR) in the form of text and heart rate signal (ECG) with medical images. To improve the security, the text data and ECG signal are encrypted before interleaving in the frequency domain. Predicative coding techniques such as adaptive delta modulation (ADM) and differential pulse code modulation (DPCM) are applied for ECG signal encryption and compression. The quality of the watermarked image is assessed using peak signal-to-noise ratio and normalised mean square error. The qualitative analysis was carried out by taking opinions from the physicians and radiologist and it was found that the perceptual integrity of the medical image was preserved. The experimental results on different medical image modalities (MRI, MRA, CT) are analysed, which demonstrates the efficiency and transparency of the proposed scheme as compared to the earlier reported methods. Keywords Medical image watermarking

DCT ADM DPCM Encryption

Mousami Turuk (&) A. Dhande Department of E&Tc, Pune Institute of Computer Technology, Pune 411043, India e-mail: [email protected] © Springer Science+Business Media Singapore 2016 N.R. Shetty et al. (eds.), Emerging Research in Computing, Information, Communication and Applications, DOI 10.1007/978-981-10-0287-8_61

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Mousami Turuk and A. Dhande

1 Introduction A health information system (HIS) distributes and retrieves vast amounts of medical data in terms of biosignals and text data of patients. It is imperative to preserve the authentication of patient information while transmitting medical images and patient data using an insecure medium such as the Internet. A patient’s personal data are highly sensitive in nature and therefore demand additional measures for medical confidentiality and protection against unauthorised access. Source authentication and data integrity are key aspects of health data management and distribution [1, 2]. The security of medical information derived from strict ethics and legislative rules has to fulfil the characteristics, namely, confidentiality, integrity, and authentication [3]. With the recent trends and the upcoming developments of multimedia and communication technologies, new means to share and handle medical data over the Internet are provided. It is crucial to secure the patient’s healthcare information against illicit access. A huge amount of digital medical images, biosignals, and text data need to be exchanged between hospitals and needs efficient and reliable storage and transmission techniques. Interleaving one form of data in the form of 1D signal, or text document, over digital medical images can combine the advantages of data security with efficient memory utilisation [1, 4]. The research community considers digital watermarking as a complementary security mechanism for improving medical image security and is therefore a thrust area of research. Digital watermarking has the potential of providing solutions for different issues associated with medical data management, dealing the new challenges arising from the exponentially growing amount of information. Digital watermarking has gained a foothold in the healthcare sector.

2 Related Work Researchers proposed different watermarking techniques and reported the findings in the literature to assure confidentiality and authentication needs. The wavelet histogram shifting technique to hide the binary hospital logo was implemented using the locations of the thresholds and zero-points in the histogram for inserting the watermark [5]. Spatial domain approaches have also been explored for patient identification using a triangular function [6]. Another spatial domain approach includes analysis of mammogram images to preserve authenticity and integrity using a digital signature and hash function [7]. In another work, a genetic algorithm is utilised to embed the watermark or textual data around the region of interest (ROI) of a medical image. The signature image and fragile watermark are embedded in the non-ROI part of the medical image [8]. Efforts are reported to preserve the confidentiality using a quantisation approach to hide the multiple watermarks [9].

Interleaving Scheme for Medical Image Authentication

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Spatial and frequency domain analysis was reported to hide encrypted text or a graphical signal [10]. To the best of our knowledge, interleaving both, encrypted text and encrypted ECG signal, in a medical image has received less attention by researchers. This paper presents a novel double watermarking approach using encrypted text and an encrypted ECG signal. The proposed technique interleaves EPR and ECG signals in medical images using a discrete cosine transform (DCT) approach. The experimental result proves the benefits of the proposed research to improve authentication and confidentiality of patient data and additionally saves the bandwidth for transmission and requires less memory for HIS.

3 Interleaving Approach The framework of interleaving authentication credentials in the medical image is detailed in Fig. 1. Patient information in the form of a text document and ECG signal is encrypted before watermarking, to boost security [11]. The discrete cosine transform is a signal decomposition technique used to convert an image from the spatial domain to the frequency domain. The DCT is calculated for block size of N × N pixels using the following equation [12], DCTði; jÞ ¼ DðiÞ Dð jÞ 8 qffiffiffi > < N1 for DðiÞ; Dð jÞ ¼ qffiffiffi > : 2 for N

ð2x þ 1Þip ð2y þ 1Þjp f ðx; yÞ cos cos 2N 2N y¼0

N ¼0 X N ¼0 X x¼0

i; j ¼ 0 i; j ¼ 1; 2; . . .N 1 ð1Þ

Note that DCT(i, j) represents the coefficient at coordinate (i, j) in the DCT-transformed block and f(x, y) is the pixel value at coordinate (x, y) in the original block. The grayscale image is shown in Fig. 2a and its discrete cosine transform is depicted in Fig. 2b. The principle advantage of discrete cosine transformation is the removal of redundancy between neighbouring pixels. This leads to uncorrelated transform coefficients which can be encoded independently. The whole DCT image is divided in three broad regions: DC or low-frequency coefficients, middle-frequency coefficients, and high-frequency coefficients. DCT provides strong energy compaction; that is, the energy of an image is compacted as a part of the low frequency [12]. To preserve the imperceptibility of a reconstructed

M edical Image (CT, M RA, M RI)

Apply DCT Transform

Interleave encrypted ECG with and Text

Interleaved Medical Image

Fig. 1 Proposed scheme for text document and ECG signal interleaving for image authentication

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Fig. 2 Discrete cosine transform (DCT) of an image: a Original medical image; b DCT of an image

image, low-frequency coefficients of DCT are not altered as they contain important information. Therefore, randomly selected high-frequency coefficients and low-frequency coefficients are chosen for interleaving. An LSB modification technique is used for interleaving encrypted patient text data and the ECG signal in the randomly chosen DCT coefficients as the resulting image shows minimum degradation [10]. The hidden contents are retrieved using reverse process of LSBs extraction and concatenation. The process of ECG and text encryption is similar to our earlier reported work [13] and is briefly presented here for completeness.

3.1

Encryption of Text Document

An electronic patient record (EPR) contains the patient information in the form of history or diagnostic details in the form text file as shown in Fig. 3a. Literature reports two popular approaches namely Logarithmic and RSA (Rivest–Shamir– Adleman) for text encryption. To enhance the security ASCII codes of the text document are encrypted by these techniques before interleaving the text in the original image in the proposed method. The logarithmic encryption is chosen for encryption in proposed method due to its simplicity. The logarithmic encryption can be scientifically presented as [10] EPRenc ¼ ðlogðEPRorg 2Þ 100Þ300

ð2Þ

where EPRenc represents the encrypted text and EPRorg represents the ASCII code of the original text file. EPRenc is an eight-bit integer which is interleaved in the medical image. The eight bits of the encoded text file are divided in two bit streams, each having four bits and these are placed in LSBs of DCT coefficients. The decrypted text is acquired by [10]


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Fig. 3 EPR encryption by logarithmic function: a Original text file; b Encrypted text file

EPROrg ¼ exp

EPRenc þ 300 logð2Þ : 100

ð3Þ

The EPR text original and encrypted form are shown in Fig. 3a, b and it was found that the same amount of memory space is utilized by both also, in spite of EPRenc being rounded off exact reconstruction is achieved. The RSA algorithm is the most popular public key encryption algorithm for secure communication. The pair of numbers (e, n) is used as the public key and the pair of numbers (d, n) is known as the private key which is secret. The number e and d is the public and private exponent, and n is known as the modulus. The message ‘m’ is encrypted as C ¼ me ðmod nÞ

ð4Þ

and the decrypted message is obtained by m ¼ C d ðmod nÞ

3.2

ð5Þ

Encryption of Biosignal (ECG)

In our approach the ECG signal is embedded in encrypted form. To store an analog ECG signal in digital form, the ECG signal is sampled at the suitable rate to retain relevant details of peaks and the frequency. The sampled signal is converted into digital form whose dynamic range is determined by the output of an analog-to-digital converter. The basic need of the heart rate signal encryption is for the reduction of bandwidth. The heart rate signal is of 16 bits. It will take bandwidth 16 × fs where fs is the sampling frequency. Predictive coding techniques, adaptive

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delta modulation (ADM), and differential pulse code modulation (DPCM) are applied on the ECG signal for encryption. The heart rate signal is encrypted to 1 bit using ADM and to 4 bits using DPCM [10]. DPCM is the predictive coding technique which estimate predicted sample xeq (k) [14] from the linearly weighted past samples xq(k − i) with weights Ci. xeq ¼

n X

ci xq ðk iÞ

ð6Þ

i¼1

The process is expressed using Eq. (6). The quantizer produces the prediction error eq(k) using the unquantised error x(k) − xeq (k) which is further encoded in 4 bits error signal by DPCM. This error output is the encrypted version of heart rate signal. The original and reconstructed ECG signals are shown in Fig. 4a, c respectively, and Fig. 4b depicts the encrypted heart rate signal. The ADM is popular than DPCM as it encrypts the signal in single bit eq(k) and reduces the slope overload effect with minimizing granular noise. The ADM uses variable step size controller which changes the gain depending on previous and present values of eq(k) by a factor of K, where K is in the span 1 < K < 2. The process of gain change is elaborated using Eq. (7) [14].

Fig. 4 DPCM technique: a Original ECG signal; b Encrypted ECG signal; c Reconstructed ECG signal


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Fig. 5 ADM technique: a Original ECG signal; b Encrypted ECG signal; c Reconstructed ECG signal

zðk Þ ¼

zðk 1Þ KeðkÞ ¼ eq ðk 1Þ zðk 1Þ=Keq ðkÞ 6¼ eq ðk 1Þ

ð7Þ

Here z(k) and z(k − 1) are refereed as present and previous sample gain. The new predicted sample is expressed as [14]: xeq ðk Þ ¼ xeq ðk 1Þ þ zðk 1Þ eq ðk 1Þ

ð8Þ

The present sample x(k) is compared with the predicated sample xeq (k). A comparator output eq(k) is ±Δ, where Δ is the step size and positive and negative step size is determined based on comparison result. The original and reconstructed ECG signals are shown in Fig. 5a, c respectively, and Fig. 5b shows the encrypted signal.

4 Results Samples of ECG signals (2000 bytes) publically available at Massachusetts Institute of Technology (MIT-DB) and a text of 260 bytes are embedded in the medical test images. Various medical imaging modalities (60 images of MRI, MRA and CT) (256 X 256 pixels) were chosen to verify the efficacy of the developed algorithm.

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Peak signal-to-noise ratio (PSNR) is calculated to check the imperceptibility of the interleaved image which is calculated between the original image (Q) and the interleaved image (Q′). PSNR ¼ 10:log10

2552 MSE

ð9Þ

where mean square error (MSE) is calculated as MSE ¼

M X M 1 X 2 ½Qði; jÞ Q0 ði; jÞ M:M i¼1 j¼1

ð10Þ

Quantitative assessment of the algorithm is carried out to check alteration in the original image content using normalised root mean square error (NRMSE), given as ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sP M PM ððQðI; J Þ Q0 ði; jÞÞ2 I¼1 NRMSEð%Þ ¼ 100 PMI¼1PM I¼1 I¼1 ðQðI; J ÞÞ2

ð11Þ

The tabulated results (Tables 1, 2, 3 and 4) show that interleaving the encrypted text improves PSNR more than interleaving the encrypted ECG. Also it is observed that RSA encryption shows growth in the PSNR and trims down NRMSE as Table 1 Results of interleaving text document

Table 2 Results of interleaving ECG signal

Table 3 Results of interleaving encrypted text with log and ECG signal with ADM and DPCM

Image

Text (log) MRI MRA

CT

Text (RSA) MRI MRA

CT

PSNR NRMSE (%)

45.33 1.19

45.97 1.27

46.90 0.99

48.13 0.99

Image

ECG (DPCM) MRI MRA CT

ECG (ADM) MRI MRA

CT

PSNR NRMSE (%)

38.52 2.68

40.87 2.00

42.44 1.91

Image

Text (log) + ECG (DPCM) MRI MRA CT

Text (log) + ECG (ADM) MRI MRA CT

PSNR NRMSE (%)

39.04 2.47

39.99 2.21

48.28 1.85

44.34 2.91

43.03 3.39

40.80 2.31

39.83 2.58

50.71 1.40

49.69 1.57

46.24 2.34

41.08 2.23

Interleaving Scheme for Medical Image Authentication Table 4 Results of interleaving encrypted text with RSA and ECG signal with ADM and DPCM

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Image

Text (RSA) + ECG (DPCM) MRI MRA CT

Text (RSA) + ECG (ADM) MRI MRA CT

PSNR NRMSE (%)

39.26 2.41

40.27 2.14

43.56 3.19

40.21 2.47

47.44 2.04

41.61 2.10

compared to logarithmic encryption. ECG signal encryption using ADM is preferred to DPCM as it improves the PSNR. Results of interleaving both text and ECG signal maintain the perceptual transparency of an image, as PSNR value is not hampered even though the embedding capacity is increased. In the dual interleaving approach, the ECG encrypted by ADM and text with log show improvement in PSNR and smaller values of NRMSE in comparison with the ECG encrypted by DPCM and text with log. The ECG encrypted by ADM and text with RSA overruled in PSNR enhancement than ECG encrypted by DPCM and text with RSA. In the dual interleaving approach, hiding the ECG encrypted by ADM and text with RSA outperforms others on the basis of improving security, increasing the hiding capacity, and better imperceptibility. Figures 6, 7, 8 and 9 show that imperceptibility of the original medical image is preserved by interleaving the encrypted text file and ECG signal which is ascribed to the fact that the LSB modification of DCT coefficients in middle and high frequency regions does not hamper the picture quality significantly. Robustness of the algorithm is checked by using benchmark attacks including rotating (−30°), sharpening, blurring, and salt and pepper noise. Image processing attacks such as rotation, sharpening, blurring, and salt and pepper noise are commonly observed when the images are captured or saved in HIS. The robustness of the algorithm is analysed for text encryption using RSA, ECG signal encryption using ADM, and a combined RSA–ADM approach as the tabulated results show that better perceptibility is achieved by these techniques, based on PSNR values.

Fig. 6 Results of interleaving text with RSA and ECG with DPCM in the CT image: a Original image; b Interleaved image

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Fig. 7 Results of interleaving text with RSA and ECG with ADM in the MRI image: a Original image; b Interleaved image

Fig. 8 Results of interleaving text with log and ECG with ADM in the MRA image: a original image; b Interleaved image

Fig. 9 Results of interleaving text with log and ECG with DPCM in the MRI image: a Original image; b Interleaved image

Figure 10 shows the change in PSNR after rotating, sharpening, blurring, and salt and pepper attacks on 10 MRI images with DCT-based interleaving of the text encrypted with RSA, interleaving of the ECG signal encrypted with ADM, and interleaving of text encrypted with RSA and ECG encrypted with ADM. Graphical results in Fig. 10a–c demonstrate the effects of various attacks on the PSNR of the images. It is apparent that the rotation attack and the salt and pepper noise hamper


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the imperceptibility of the interleaved image more as compared to the sharpening and blurring attacks. One of the probable reasons for this is the short of synchronisation in the process of embedding. Figures 11 and 12 show the histograms of the original and interleaved (with encrypted text with RSA and ECG signal with ADM) CT image. It can be observed that the shape of the histograms is almost similar. In Fig. 12 the change in the pixel value is noticed based on the interleaving bit.

Fig. 10 PSNR of 10 MRI images after rotating, sharpening, blurring, and salt and pepper attacks

Fig. 11 Histogram of original CT image

Fig. 12 Histogram of interleaved CT image with encrypted text with RSA and ECG signal with ADM

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5 Conclusion Digital watermarking has the prospective to give balancing solutions for key aspects of HIS including image authentication and efficient medical data storage. A double watermarking approach using an encrypted ECG biosignal and EPR for medical image authentication is presented for efficient and secure storage. The proposed interleaving technique is analysed using 20 images of each modality. The proposed DCT-based approach is efficient as it does not compromise on the diagnostic details of the medical images and also reduces transmission overhead and excessive memory utilisation. Experimental results show that the perceptual integrity of the medical image is preserved even though the hiding capacity is gradually increased by hiding only the text, only the ECG signal, and both the ECG signal and text based on observed PSNR values which are not much affected. Moreover, the histograms of the original and interleaved image are similar, thus preserving the gray-level information. The smaller value of NRMSE confirms the original diagnostic information of the image is retained. The comparison of our approach with previously addressed frequency domain method [10] shows smaller NRMSE values (less than 5 %) even though hiding capacity is increased. Our further research aims to analyse the different frequency domain techniques with the proposed scheme. Acknowledgments The Omega MRI center and database to carry out the Suhas Panmand, and Mr.

authors would like to thank Dr. Abhimanyu Kelkar, Director Dr. Wani, Director Medicare Lab, for providing the medical image research and for valuable discussions. Thanks also to Dr. Munot, Mr. A. Bhinge for valuable suggestions and help.

References 1. Privacy and Security Committee: Security and privacy: An introduction to HIPAA. Medical Imaging Informatics Section, NEMA, 10 April 2001 2. Giakoumaki. A.: Multiple image watermarking applied to health information management. IEEE Trans. Inf. Technol. Biomed. 722–732 (2006) 3. Coatrieux, G., Maitre, H., Sankur, B.: Strict integrity control of biomedical images. In: Proceedings SPIE Security Watermarking Multimedia Contents III, SPIE 2001, vol. 4314, pp. 229–240, Jan 2001 4. Image Data management Committee: ACR Technical Standard for Digital Image Data Management. American College of Radiology Standard, Reston, VA (2002) 5. Golpira, H., Danyali, H.: Reversible blind watermarking for medical images based on wavelet histogram shifting. In: 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 31–36, Dec 2009 6. Velumani, R., Seenivasagam, V.: A reversible blind medical image watermarking scheme for patient identification, improved telediagnosis and tamper detection with a facial image watermark. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–8, Dec 2010 7. Zhou, X.Q., Huang, H.K., Lou, S.L.: Authenticity and integrity of digital mammography images. IEEE Trans. Med. Imaging 20, 784–791 (2001)


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8. Shih, F.Y., Wu, T.: Robust Watermarking and compression for medical images based ongenetic algorithms. J. Inf. Sci. 175(3), 200–206 9. Giakoumaki, A., Pavlopoulos, S., Koutsouris, D.: A multople watermarking scheme applied to medical image management. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco, CA, USA, pp. 3241–3244, Sept 2004 10. Achary, U.R., Niranjan, U.C., Iyengar, S.S., Kannathal, N., Min, L.C.: Simultaneous storage of patient information with medical images in the frequency domain. Elsevier, Computer Methods and Programs in Biomedicine, pp. 13–19, Feb 2004 11. Acharya, U.R., Anand, D., Bhat, P.S., Niranjan, U.C.: Compact storage of medical image with patient information. IEEE Trans. Inf. Technol. Biomed. 5(4), 320–323 (2001) 12. Khayam, S.A.: The discrete cosine transform (DCT): Theory and Application. Michigan State Univeersity, 10 March 2003 13. Turuk, M., Dhande, A.P., Kalgaonkar, P.P.: Performance evaluation of frequnecy domain techniques for efficient storage of patient information with medical images. In: 2014 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 173–178, Dec 2014 14. Carlson, B.: Communication Systems: An Introduction To Signals And Noise In Electrical Communication Systems, 4 edn. pp. 516–518. McGrawHill, NY (2002)