Total views : 147

Enhanced Data Security and Integrity using Contourlet Transform for Medical Images

Affiliations

  • Department of Electronics and Communication Engineering, Pondicherry Engineering College, Pillaichavadi – 605014, Puducherry, India

Abstract


Objective: The objective of this framework is to analyze the security, compression ratio and integrity by using segmentation algorithm and transformation to extract the tumor region from MRI brain image and to observe its performance metrics. Methods/Statistical Analysis: This paper offers a futuristic healthcare solution to encompass of segmenting the ROI using Bhattacharya coefficient algorithms and successively applying the modified EMD steganography method centered on contourlet transform. This framework also challenges to verify the integrity of ROI using SHA-1, precisely senses any variation in ROI, furthermore, it ensures robustness of the entrenched data in non-region of interest and mends ROI perfectly for investigation. Lastly the whole image is encrypted with modified Logistic map encryption in order to afford overall security. Findings: This work recapitulates the contrast of distinct embedding algorithms viz. Least Significant Bit - Discrete Cosine Transform (LSB-DCT) and no-shrinkage F5-Integer Wavelet Transform (nsF5-IWT) with Improved Exploiting Modification Direction-Contourlet Transform (IEMD-CT) for embedding processes. The performance analysis of integrity check during transmission is verified using Secure Hash Algorithm 1 (SHA-1). Experimental endings deliberate Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Square Error (MSE), Bit Error Rate (BER), Signal to Noise Ratio (SNR) as performance metrics and found the effectiveness of the proposed framework over conventional methods. Application/Improvements: This framework can be exploited in telemedicine applications in order to obtained with meticulousness in tumor location for effective healthcare services.

Keywords

Brain Tumor, Confidentiality, Contourlet Transform, Integrity, MRI Image, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Tumor Extraction

Full Text:

 |  (PDF views: 150)

References


  • Raskovic D, Martin T and Jovanov E. Mobile monitoring application for wearable computing. Journal of the Computer. 2004; 47:4495–504.
  • Hong JH, Kim NJ, Cha EJ and Lee TS. Development brief of a body area network for ubiquitous healthcare: An introduction to ubiquitous biomedical systems development center. Journal of Biomedical Engineering Research. 2005; 26:331–5.
  • Do MN and Vetterli M. Orthonormal Finite Ridge let transform for image compression. ICIP’2000. Vancouver, Canada; 2000 Sep.
  • Candes EJ and Donoho DL. New tight frames of curvelets and optimal representation of Objects with Smooth Singularities. 2002.
  • Vallathan G, Balachandar K and Jayanthi K. Provisioning of enhanced medical data security and quality for telemedicine applications. International Conference on Recent Trends in Engineering and Material sciences. Jaipur; 2016 mar.
  • Fylakis A, Keskinarkaus A, Kiviniemi V and Seppanen T.Reversible blind data hiding for verifying integrity and authenticating MRI and X-Ray images. Proceedings of the IEEE 9th International Symposium on Medical Information and Communication Technology (ISMICT’15). Kamakura, Japan; 2015. p. 185–9.
  • An L, Gao X, Li X, Tao D, Deng C and Li J. Robust reversible watermarking via clustering and enhanced pixel- wise masking. IEEE Transactions on Image Processing. 2012; 21(8):3598–611. Crossref
  • Coatrieux G, Huang H, Shu H, Luo L and Roux C. A watermarkingbased medical image integrity control system and an image moment signature for tampering characterization.IEEE Journal of Biomedical and Health Informatics. 2013; 17(6):1057–1067. Crossref
  • Priya RL and Sadasivam V. Protection of Health Imagery by Region Based Lossless Reversible Watermarking Scheme.The Scientific World Journal. 2015; 2015:10, Article ID 489348.
  • Guo L, Ni J and Shi YQ. Uniform embedding for efficient JPEG steganography. IEEE transactions on Information Forensics and Security. 2014 may; 9(5):814–25. Crossref
  • Jung K-H et al. Improved Exploiting Modification Direction Method by Modulus Operation. International Journal of Signal Processing, Image Processing and Pattern. 2009 mar; 2(1):79–88.
  • Seyedzade SM, Mirzakuchaki S and Atani RE. A novel image encryption algorithm based on hash function.6th Iranian Conference on Machine Vision and Image Processing. 2010; p. 1–6. ISSN: 2166- 6776.
  • Chang C and Chang YF. Signing a digital signature without using one-way hash functions and message 146 redundancy schemes. IEEE Transactions. 2004; 8:485–7.
  • Schneier B. Cryptography: Theory and Practice. CRC Press, Boca Raton; 1995.
  • Wong K-W. Image Encryption Using Chaotic Maps. Intel.Computing Based on Chaos, Science. 2009; 184:333–54
  • Zhu A-H andLi L. Improving for Chaotic Image Encryption Algorithm Based on Logistic Map. 2nd Conference on Environmental Science and Information Application Technology; 2010.
  • Vallathan G, Balachandar K and Jayanthi K. Segmentation based Security Enhancement for Medical Images. Special Issue in International Journal of Computer Science and Information Security (IJCSIS). 2016; 14 CIC 2016:55–9.ISSN 1947-5500.
  • Sreedevi B, Kumar TA and Rao KK. A Novel Denoising and Segmentation of Brain Tumors in MRI Images. Indian Journal of Science and Technology. 2016 Nov; 9(44):1–7.Crossref
  • Chang C and Chang YF. Signing a digital signature without using one-way hash functions and message 146 redundancy schemes. IEEE Transactions. 2004; 8:485–7.
  • Kumar N and Kalpana V. A Novel Reversible Steganography Method using Dynamic Key Generation for Medical Images. Indian Journal of Science and Technology. 2015 Jul; 8(16):1–10. Crossref
  • Ulutas G, Ulutas M and Nabiyev VV. Secret image sharing with reversible Capabilities. International Journal of Internet Technology and Secured Transactions. 2012; 4(1):1–11. Crossref
  • Eswaraiah R and Reddy ES. Robust medical image watermarking technique for accurate detection of tampers inside region of interest and recovering original region of interest. IET Image Process. 2015; 9(8):615–25.
  • Guo L, Ni J and Shi YQ. Uniform embedding for efficient JPEG steganography. IEEE transactions on Information Forensics and Security. 2014 may; 9(5):815–25. Crossref

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.