Abstract
In order to protect the image contents, many reversible medical image watermarking schemes have been proposed, although reversibility guaranteed the lossless of the cover image, but it also has some shortcomings such as it is vulnerable to geometrical attacks. So a novel robust watermarking scheme for medical image based on the combination Redundancy Discrete Wavelet Transform (RDWT) and Singular Value Decomposition (SVD) is proposed in this letter. Different from the reversibility, which guarantees the perceptional lossless, the proposed scheme achieves satisfied visual quality by exploiting the visually masking property of RDWT, in the meantime, Speeded-Up Robust Features (SURF) and Random sample consensus (RANSAC) based rotation correction scheme is put forward, which can be used to restore the attacked image to the original state. The experimental results show that the proposed scheme has the large amounts of embedding capacity; it is also robust against rotation attacks; and the perceptional quality of watermarked image meets the need of usage in medical images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rabbani, M., Joshi, R.: An overview of the JPEG 2000 still image compression standard. Signal Processing:Image Communication 17, 3–48 (2002)
Rosslyn, S.: Digital Imaging and Communications in Medicine (DICOM) Part 5: Data Structures and Encoding (2011)
Fallahpour, M., Megias, D., Ghanbari, M.: Reversible and high-capacity data hiding in medical images. IET Image Processing 5, 190 (2011)
Weng, S., Pan, J.-S., Gao, X.: Reversible Watermark Combining Pre-processing Operation and Histogram Shifting. Journal of Information Hiding and Multimedia Signal Processing 3(4), 320–326 (2012)
Lin, S.-L., Huang, C.-F., Liou, M.-H., Chen, C.-Y.: Improving Histogram-based Reversible Information Hiding by an Optimal Weight-based Prediction Scheme. Journal of Information Hiding and Multimedia Signal Processing 4(1), 19–33 (2013)
Horng, G., Huang, Y.-H., Chang, C.-C., Liu, Y.: (k, n)-Image Reversible Data Hiding. Journal of Information Hiding and Multimedia Signal Processing 5(2), 152–164 (2014)
Lee, H., Lee, J., Kim, N., Kim, S.J., Shin, Y.G.: Robust feature-based registration using a Gaussian-weighted distance map and brain feature points for brain PET/CT images. Computers in Biology and Medicine 38, 945–961 (2008)
Na, W., Yamaguchi, K., Cedillo-Hernandez, M., Miyatake, M.N., Perez-Meana, H.: Robust Image Watermarking Using Feature Points and Image Normalization. In: 2010 IEEE Electronics, Robotics and Automotive Mechanics Conference, pp. 313–318 (2010)
Tsai, J.-S., Huang, W.-B., Kuo, Y.-H., Horng, M.-F.: Joint robustness and security enhancement for feature-based image watermarking using invariant feature regions. Signal Processing 92, 1431–1445 (2012)
Rastegar, S., Namazi, F., Yaghmaie, K., Aliabadian, A.: Hybrid watermarking algorithm based on Singular Value Decomposition and Radon transform. AEU - International Journal of Electronics and Communications 65, 658–663 (2011)
Lai, C.-C., Tsai, C.-C.: Digital Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition. IEEE Transactions on Instrumentation and Measurement 59, 3060–3063 (2010)
Li, Q., Yuan, C.: Adaptive DWT-SVD domain image watermarking using human visual model. The Communication Technology, 1947–1951 (2007)
Ling, H.-C., Phan, R.C.-W., Heng, S.-H.: On the security of a hybrid watermarking algorithm based on singular value decomposition and Radon transform. AEU - International Journal of Electronics and Communications 65, 958–960 (2011)
Fowler, J.: The redundant discrete wavelet transform and additive noise. IEEE Signal Processing Letters 12, 629–632 (2005)
Lagzian, S., Soryani, M., Fathy, M.: Robust watermarking scheme based on RDWT-SVD: Embedding data in all subbands. In: 2011 International Symposium on Artificial Intelligence and Signal Processing (AISP), pp. 48–52 (2011)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (SURF). Computer Vision and Image Understanding 110(3), 346–359 (2008)
Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24 (1981)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society 13, 600–612 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Gao, L., Gao, T., Sheng, G., Zhang, S. (2014). Robust Medical Image Watermarking Scheme with Rotation Correction. In: Pan, JS., Snasel, V., Corchado, E., Abraham, A., Wang, SL. (eds) Intelligent Data analysis and its Applications, Volume II. Advances in Intelligent Systems and Computing, vol 298. Springer, Cham. https://doi.org/10.1007/978-3-319-07773-4_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-07773-4_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07772-7
Online ISBN: 978-3-319-07773-4
eBook Packages: EngineeringEngineering (R0)