A New Watermarking Scheme for Medical Images with Patient’s Details

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    A brain tumor is a mass of cells in your brain that are not normal.Some brain tumors contain cancer and others don't: Brain tumor include both, benign and malignant forms. Benign brain tumors don't have cancer cells. Malignant brain tumors have cancer cells. Differentiating malignant and benign cases is a hard task even for experienced specialists. This work presents how to extract the characteristics and features of tumor image by general segmentation methods for malignant risk computation and presents the use of digital watermarking for applications of automated tumor image analysis. Here personal information such as name, age, gender, location, ADHAAR number, contact number etc., and tumor information such as tumor types, area of the tumor, severity, and any other useful information are embedded to the tumor image. Encrypting that image with well-known encryption algorithms is also possible to avoid unnecessary nuisance from information hackers.



  • Keywords

    brain tumor; segmentation; k-means clustering; digital watermarking.

  • References

      [1] Xinpeng Zhang, “Separable Reversible Data Hiding in Encrypted Image”, IEEE Information Forensics and Security, Vol 7, No.2, April 2012.

      [2] G. Nagaraju and T. V. Hyma Lakshmi, "Image Encryption using Secret-key images and Scan patterns", International Journal of Advances in Computer, Electrical & Electronics Engineering., Vol. 2, Sp. Issue of NCIPA 2012, pp. 13-18.

      [3] Chi-Kwong Chan & L.M. Cheng (2001), “Improved Hiding Data in Images by Optimal Moderately Significant Bit Replacement”, IEEE Electronics Letters, Vol. 37, No. 16, Pp.1017–1018.

      [4] G. Naga Raju, James Vijay, “Secret-key based Separable Reversible Data-Hiding in Encrypted image,” National Conference on VLSI, Signal processing & Communications NCVSComs-2011.

      [5] X.Zhang, “Reversible data hiding in encrypted image,” IEEE Signal Processing. Lett vol. 18, no. 4, pp. 255– 258, Apr. 2011

      [6] Panduranga H.T, Naveenkumar S.K, "A novel image encryption method using 4 outof 8 code", proc.CommV'09, pp. 460-462, 2009.

      [7] Dr. P.V.RamaRaju, T. Anvesh Gandhi, G. Naga Raju , “RGB Image Steganography using Zigzag Pixel Indicator and Scan Techniques”International Journal Of Research In Electronics And Computer Engineering., Vol. 3 Issue 3, July-Sept. 2015 ISSN: 2393-9028 (print), ISSN: 2348-2281 (online) Pp103-Pp107

      [8] V. S. Verma and R. K. Jha, “An Overview of Robust Digital Image Watermarking”, IETE Technical Review, vol. 32, pp. 479-496, Nov 2015.

      [9] S. A. Parah, J. A. Sheikh, U. I. Assad, and G. M. Bhat, “Realisation and robustness evaluation of a blind spatial domain watermarking technique”, International Journal of Electronics, vol. 104, no. 4, pp. 659-672, 2017.

      [10] Q. T. Su, Y. G. Niu, Q. J. Wang, and G. R. Sheng, "A blind color image watermarking based on DC component in the spatial domain," Optik, vol. 124, no. 23, pp. 6255-6260, 2013.

      [11] A. Upadhyay and M. Dave, “Robust and Imperceptible Color Image Watermarking for Telemedicine Applications”, 2016 IEEE International Conference on Computing, Communication and Automation, pp. 1104- 1109, 2016.

      [12] S.-J. Horng, D. Rosiyadi, T. Li, et al., “A blind image copyright protection scheme for e-government”, Journal of Visual Communicationand Image Representation, vol. 24, no. 4, pp. 1099–1105, 2013.

      [13] S. Rawat, and B. Raman, “A blind watermarking algorithm based on fractional Fourier transform and visual cryptography”, IEEE Signal Processing, vol. 92, no. 6, pp. 1480–1491, 2012.

      [14] R. O. Preda, D. N. Vizireanu, S. Halunga, “Active image forgery detection scheme based on semi-fragile watermarking”, Revue Roumaine des Sciences Techniques-SerieElectrotechnique et Energetique, Vol. 61, Issue 1, pp. 58-62, ISSN: 0035-4066, 2016.

      [15] P. Cavalcanti, J. Scharcanski, L. Di Persia, and D. Milone, "An ICAbased method for the segmentation of pigmented skin lesions in macroscopic images," in Proc. IEEE Annu. Int. Conf. Eng. Med. Biol. Soc., pp. 5993-5996, 2011.

      [16] Cavalcanti, P.; Scharcanski, J. A Coarse-to-Fine Approach for Segmenting Melanocytic Skin Lesions in Standard Camera Images. Computer Methods and Programs in Biomedicine, Vol. 112, No. 3, pp. 684-93, 2013.

      [17] N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst., Man, Cybern, vol. 9, no. 1, pp. 62–66, Jan. 1979.

      [18] P. V. Ramaraju, G. N. Raju and P. R. Krishna, "Image encryption after hiding (IEAH) technique for color images," 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES),Paralakhemundi, 2016, pp. 1202-1207. doi: 10.1109/SCOPES.2016.7955631

      [19] S. Na, L. Xumin and G. Yong, "Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm," 2010 Third International Symposium on Intelligent Information Technology and Security Informatics, Jinggangshan, 2010, pp. 63-67.
      doi: 10.1109/IITSI.2010.74

      [20] J. Wang and X. Su, "An improved K-Means clustering algorithm," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 44-46.
      doi: 10.1109/ICCSN.2011.6014384

      [21] Lu Ming, "Image segmentation algorithm research and improvement," 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), Chengdu, 2010, pp. V5-211-V5-214. doi: 10.1109/ICACTE.2010.5579114




Article ID: 18194
DOI: 10.14419/ijet.v7i3.31.18194

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.