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Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 839161, 8 pages
http://dx.doi.org/10.1155/2012/839161
Research Article

Nested Quantization Index Modulation for Reversible Watermarking and Its Application to Healthcare Information Management Systems

1Department of Electrical Engineering, National Central University, Chungli 320-01, Taiwan
2Department of Electronics Engineering, Chung Hua University, Hsinchu 300-12, Taiwan
3Department of Computer Science and Information Engineering, National United University, Miaoli 360-03, Taiwan

Received 25 August 2011; Accepted 15 September 2011

Academic Editor: Shengyong Chen

Copyright © 2012 Lu-Ting Ko et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Digital watermarking has attracted lots of researches to healthcare information management systems for access control, patients' data protection, and information retrieval. The well-known quantization index modulation-(QIM-) based watermarking has its limitations as the host image will be destroyed; however, the recovery of medical images is essential to avoid misdiagnosis. In this paper, we propose the nested QIM-based watermarking, which is preferable to the QIM-based watermarking for the medical image applications. As the host image can be exactly reconstructed by the nested QIM-based watermarking. The capacity of the embedded watermark can be increased by taking advantage of the proposed nest structure. The algorithm and mathematical model of the nested QIM-based watermarking including forward and inverse model is presented. Due to algorithms and architectures of forward and inverse nested QIM, the concurrent programs and special processors for the nested QIM-based watermarking are easily implemented.

1. Introduction

Digital watermarking is a scheme of embedding data in an image called host image for the purpose of copyright protection, integrity check, and/or access control [16]. Some of the requirements of digital watermarking are transparency, robustness, and capacity. Specifically, transparency means that the watermark embedded in the host image is imperceptible to human eyes, robustness means the resistance of the watermark to malicious attacks, and capacity denotes the amount of data that can be hidden in the host image. Digital watermarking has been applied to many applications [710].

For medical images, such as radiography, magnetic resonance imaging (MRI), nuclear medicine imaging, photo acoustic imaging, tomography, and ultrasound, the conventional watermarking schemes are not suitable due to the distortion problem, which can lead to misdiagnosis [1114]. In order to provide the requirements of confidential data protection and intact information retrieval [1524], watermarking with legal and ethical functionalities is desirable especially for the medial images applications. For diagnostic information retrieving and host image reconstruction, the reversible watermarking can be achieved by using many common used techniques, which are based on the characteristics of nonlinear time series [2530]. More specifically, confidential data such as patients’ diagnosis reports can be taken as watermark data and then embedded in the host image by using digital watermarking with authorized utilization. Thus, digital watermarking can be used to facilitate healthcare information management systems.

In this paper, we propose a novel scheme called the nested quantization index modulation-(QIM-) based watermarking for the healthcare information management applications. The reminder of the paper proceeds as follows. In Section 2, the conventional QIM-based watermarking is reviewed briefly. Section 3 describes the nested QIM-based watermarking. Experimental results on medical images are presented in Section 4. Conclusion is given in Section 5.

2. Review of Quantization Index Modulation

Figure 1 depicts the conventional quantization index modulation-(QIM-) based watermarking scheme [31], where , , , , and denote the watermark, the secret key, the coded watermark, the host image, and the watermarked image, respectively. For the sake of simplicity, let us consider monochromatic images with 256 grey levels, and the size of the watermark is one-fourth of that of the host image. The secret key is used to map the binary representation of the watermark onto the host image, for example, Figure 2 depicts the binary representation of a watermark pixel that is mapped onto a segment using a given secret key.

839161.fig.001
Figure 1: The conventional QIM-based watermarking (: the watermark, : the secret key, : the coded watermark, : the quantization step, : the host image, : the watermarked image).
839161.fig.002
Figure 2: The secret key used for mapping the watermark onto the host image.

Figure 3 shows the operation of the QIM block, in which the grey levels of the host image, , ranging between and will be quantized into if the corresponding pixels of the coded watermark, , are bit 1; otherwise they are quantized into if the corresponding pixels are bit 0. For the grey levels of that are between and , they will be quantized into or depending on the corresponding pixels of being bit 1 or 0, respectively. Note that denotes the quantization step, , and is an integer number.

fig3
Figure 3: Operations of the QIM scheme for the coded watermark pixels being (a) bit 1 and (b) bit 0, respectively.

It is noted that the watermarked image, , can be written as where denotes the position index of pixels, and the coded watermark, , can be obtained by as shown in Figure 4. Together with the secret key, , the watermark, , can be exactly extracted from the watermarked image, , as shown in Figure 5.

839161.fig.004
Figure 4: Operations of the inverse QIM scheme for the coded watermark pixels.
839161.fig.005
Figure 5: Extraction of the watermark, , from the watermarked image, , based on the conventional QIM scheme.

3. The Proposed Nested QIM for Reversible Watermarking

One of the fundamental requirements for the medical applications is recovery of the host image. As the conventional QIM-based watermarking is irreversible and the host image can not be exactly reconstructed, we propose a novel algorithm called the nested QIM algorithm for reversible watermarking. Figure 6(a) depicts the simplest nested QIM consisting of only two QIM operations, each of which is performed in the block shown in Figure 6(b), where , , , , , , , and are the watermark, the secret key, the coded watermark, the input host image, the output watermarked image, the quantization, the quantization error, and the quantization step at the th stage of a -level nested QIM, respectively. The original host image and the final watermarked image denoted by and are taken as the input and the output of the first stage.

fig6
Figure 6: (a) The simplest nested QIM with two blocks (: the host image, : the intermediate watermarked image, : the final watermarked image, and : the watermarks, and : the secret keys, and : the quantization steps, and : the quantization errors, and : the normalized quantization errors). (b) The block used in the nested QIM.

As one can see, QIM is a nonlinear function, and we have The final watermarked image, is thus derived as

The original host image and the embedded watermark images can be exactly reconstructed from the watermarked image, , by using the inverse nested QIM shown in Figure 7(a), where the inverse block is shown in Figure 7(b).

fig7
Figure 7: (a) The inverse nested QIM with two inverse blocks. (b) The block used in the inverse nested QIM for watermark extraction.

Based on the data flow of Figures 7(a) and 7(b), we have Together with (3) and (4), the original host image, , is thus obtained by

The above-mentioned equations can be generalized as follows: where (7) are used for the nested QIM, and (8) for the inverse nested QIM. The corresponding block diagrams for embedding and extracting watermarks with the host image are shown in Figures 8 and 9, respectively.

839161.fig.008
Figure 8: The nested QIM with QIM operations for watermarking.
839161.fig.009
Figure 9: Extraction of the original host and watermark images from the watermarked image based on the nested QIM with inverse QIM operations.

4. Experimental Results on Medical Images

The nested QIM-based watermarking algorithm has been evaluated on various medical images. Figure 10 shows the test images with 256 grey levels, namely, spine, chest, fetus and head obtained by magnetic resonance image (MRI), X-ray, ultrasound, and computed tomography (CT), respectively, which are used as host images. Figure 11 shows the Lena image and Baboon image with 256 grey levels, which are used as watermarks.

fig10
Figure 10: The host images with 256 grey levels; (a) spine (MRI), (b) chest (X-ray), (c) fetus (ultrasonic), and (d) head (CT).
fig11
Figure 11: The (a) Lena image and (b) Baboon image with 256 grey levels used as watermarks.

The peak signal to noise ratio (PSNR) is used to evaluate the image quality [18, 31], which is defined as where denotes the mean square error. Figures 12, 13, 14, and 15 show the PSNR of the watermarked images of spine (MRI), chest (X-ray), fetus (ultrasonic), and head (CT) at various quantization steps, and . Figure 16 shows the watermarked images (first row), the reconstructed images, (second row) and the extracted watermarks (third and fourth rows) with and . It is noted that the watermarked images even with large quantization steps are almost indistinguishable from the exactly reconstructed host images.

839161.fig.0012
Figure 12: The PSNR of the watermarked image of spine (MRI) at various quantization steps and .
839161.fig.0013
Figure 13: The PSNR of the watermarked image of chest (X-ray) at various quantization steps and .
839161.fig.0014
Figure 14: The PSNR of the watermarked image of fetus (ultrasonic) at various quantization steps and .
839161.fig.0015
Figure 15: The PSNR of the watermarked image of head (CT) at various quantization steps and .
fig16
Figure 16: The watermarked images (a), the reconstructed images (b), and the extracted watermarks ((c) and (d)) with the quantization steps and .

5. Conclusion

In this paper, a novel algorithm called the nested QIM has been proposed for medical image watermarking. The capacity of the embedded watermark can be increased by taking advantage of the proposed nest structure. As the host image can be exactly reconstructed, it is suitable especially for the medical image applications. In addition, the healthcare information such as patients’ data, digital signatures, and identification codes can be well embedded in medical images. Thus, the nested QIM-based medical image watermarking is preferable to facilitate data management in healthcare information management systems.

Acknowledgment

The National Science Council of Taiwan, under Grants NSC100-2628-E-239-002-MY2, supported this work.

References

  1. I. J. Cox, J. Kilian, F. T. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia,” IEEE Transactions on Image Processing, vol. 6, no. 12, pp. 1673–1687, 1997. View at Scopus
  2. M. D. Swanson, M. Kobayashi, and A. H. Tewfik, “Multimedia data-embedding and watermarking technologies,” Proceedings of the IEEE, vol. 86, no. 6, pp. 1064–1087, 1998. View at Scopus
  3. M. Barni, F. Bartolini, and A. Piva, “Improved wavelet-based watermarking through pixel-wise masking,” IEEE Transactions on Image Processing, vol. 10, no. 5, pp. 783–791, 2001. View at Publisher · View at Google Scholar · View at Scopus
  4. G. C. Langelaar, I. Setyawan, and R. L. Lagendijk, “Watermarking digital image and video data,” IEEE Signal Processing Magazine, vol. 17, no. 5, pp. 20–46, 2000. View at Publisher · View at Google Scholar · View at Scopus
  5. C. I. Podilchuk and E. J. Delp, “Digital watermarking: algorithm and application,” IEEE Signal Processing Magazine, vol. 18, no. 4, pp. 33–46, 2001. View at Publisher · View at Google Scholar · View at Scopus
  6. C. S. Lu and H. Y. M. Liao, “Multipurpose watermarking for image authentication and protection,” IEEE Transactions on Image Processing, vol. 10, no. 10, pp. 1579–1592, 2001. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Erküçük, S. Krishnan, and M. Zeytinoǧlu, “A robust audio watermark representation based on linear chirps,” IEEE Transactions on Multimedia, vol. 8, no. 5, Article ID 1703507, pp. 925–936, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. F. Chuhong, D. Kundur, and R. H. Kwong, “Analysis and design of secure watermark-based authentication systems,” IEEE Transactions on Information Forensics and Security, vol. 1, no. 1, pp. 43–55, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Feng, N. H. Ling, G. Lin, W. Zhe, and W. J. Zhang, “Transmitter identification with watermark signal in DVB-H signal frequency network,” IEEE Transactions on Broadcasting, vol. 55, no. 3, Article ID 5170023, pp. 663–667, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Wakatani, “Digital watermarking for ROI medical images by using compressed signature image,” in Proceedings of the 35th Annual Hawaii International Conference on System Sciences, (HICSS '02), pp. 2043–2048, Big Island, Hawaii, USA, January 2002.
  11. L. T. Ko, J. E. Chen, H. C. Hsin, Y. S. Shieh, and T. Y. Sung, “Haar wavelet based just noticeable distortion model for transparent watermark,” Mathematical Problems in Engineering, vol. 2012, Article ID 635738, 14 pages, 2012.
  12. S. Y. Chen and Q. Guan, “Parametric shape representation by a deformable NURBS model for cardiac functional measurements,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 3, pp. 480–487, 2011. View at Publisher · View at Google Scholar
  13. G. Coatrieux, H. Maître, B. Sankur, Y. Rolland, and R. Collorec, “Relevance of watermarking in medical imaging,” in Proceedings of the EEE/EMBS Region 8th International Conference on Information Technology Applications in Biomedicine, (ITAB-ITIS '00), pp. 250–255, Arlington, Va, USA, November 2000. View at Scopus
  14. A. Bruckmann and A. Uhl, “Selective medical image compression using wavelet techniques,” Journal of Computing and Information Technology, vol. 6, no. 2, pp. 203–213, 1998.
  15. H. M. Chao, C. M. Hsu, and S. G. Miaou, “A data-hiding technique with authentication, integration, and confidentiality for electronic patient records,” IEEE Transactions on Information Technology in Biomedicine, vol. 6, no. 1, pp. 46–53, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. U. R. Acharya, D. Anand, P. S. Bhat, and U. C. Niranjan, “Compact storage of medical images with patient information,” IEEE Transactions on Information Technology in Biomedicine, vol. 5, no. 4, pp. 320–323, 2001. View at Publisher · View at Google Scholar · View at Scopus
  17. X. Kong and R. Feng, “Watermarking medical signals for telemedicine,” IEEE Transactions on Information Technology in Biomedicine, vol. 5, no. 3, pp. 195–201, 2001. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Giakoumaki, S. Pavlopoulos, and D. Koutsouris, “Multiple image watermarking applied to health information management,” IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 4, pp. 722–732, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. H. M. Chao, C. M. Hsu, and S. G. Miaou, “A data-hiding technique with authentication, integration, and confidentiality for electronic patient records,” IEEE Transactions on Information Technology in Biomedicine, vol. 6, no. 1, pp. 46–53, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. S. G. Miaou, C. M. Hsu, Y. S. Tsai, and H. M. Chao, “A secure data hiding technique with heterogeneous data-combining capability for electronic patient records,” in Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, pp. 280–283, Chicago, Ill, USA, July 2000. View at Scopus
  21. L. T. Ko, J. E. Chen, Y. S. Shieh, M. Scalia, and T. Y. Sung, “A novel fractional discrete cosine transform based reversible watermarking for healthcare information management systems,” Mathematical Problems in Engineering, vol. 2012, Article ID 757018, 2012.
  22. F. Hartung and M. Kutter, “Multimedia watermarking techniques,” Proceedings of the IEEE, vol. 87, no. 7, pp. 1079–1107, 1999. View at Publisher · View at Google Scholar · View at Scopus
  23. R. J. Anderson and F. A. P. Petitcolas, “On the limits of steganography,” IEEE Journal on Selected Areas in Communications, vol. 16, no. 4, pp. 474–481, 1998. View at Scopus
  24. E. T. Lin and E. J. Delp, “A review of fragile image watermarks,” in Proceedings of the ACM Multimedia Security Workshop, pp. 47–51, Orlando, Fla, USA, 1999.
  25. S. Y. Chen, H. Tong, and C. Cattani, “Markov models for image labeling,” Mathematical Problems in Engineering, vol. 2012, Article ID 814356, 18 pages, 2012.
  26. S. Y. Chen, H. Tong, Z. Wang, S. Liu, M. Li, and B. Zhang, “Improved generalized belief propagation for vision processing,” Mathematical Problems in Engineering, vol. 2011, Article ID 416963, 12 pages, 2011. View at Publisher · View at Google Scholar
  27. E. G. Bakhoum and C. Toma, “Specific mathematical aspects of dynamics generated by coherence functions,” Mathematical Problems in Engineering, vol. 2011, Article ID 436198, 10 pages, 2011. View at Publisher · View at Google Scholar
  28. E. G. Bakhoum and C. Toma, “Dynamical aspects of macroscopic and quantum transitions due to coherence function and time series events,” Mathematical Problems in Engineering, vol. 2010, Article ID 428903, 13 pages, 2010. View at Publisher · View at Google Scholar
  29. M. Li, C. Cattani, and S. Y. Chen, “Viewing sea level by a one-dimensional random function with long memory,” Mathematical Problems in Engineering, vol. 2011, Article ID 654284, 13 pages, 2011. View at Publisher · View at Google Scholar
  30. M. Li, “Four transformation patterns of two-dimensional function on wavelet basis,” International Journal Engineering and Interdisciplinary Mathematics, vol. 1, no. 2, pp. 107–110, 2009.
  31. B. Chen and G. W. Wornell, “Quantization index modulation: a class of provably good methods for digital watermarking and information embedding,” IEEE Transactions on Information Theory, vol. 47, no. 4, pp. 1423–1443, 2001. View at Publisher · View at Google Scholar · View at Scopus