Total views : 269
A Comprehensive Compression and Encryption Scheme for Secured Medical Images Communication
Background/Objectives: Among a gamut of evolving domains in medicine exchange of medicinal data and images amid multiple entities constitutes a pivotal aspect for telemedicine. An effective bandwidth allocation and management scheme is essential to accomplish the telemedicine communication requirements. This necessitates the requisite to develop and implement a compression and encryption scheme for medical images. Methods: This paper recapitulates the diverse transformation techniques employed in compression and identifies the constraints associated with the techniques. This paper summarizes the comparison of encryption methods such as Rivest Cipher 4(RC4), Rivest Cipher 2(RC2) and Data Encryption Standard (DES) in terms of time consumed to complete encryption and decryption operations. Findings: This paper considers Peak Signal-to-Noise Ratio (PSNR) and Compression Ratio (CR) as performance measures and establishes the proposed algorithms's effectiveness over Set Partitioning Hierarchical Trees (SPIHT). Application: This algorithm can be utilized for medical image compression, transfer and archiving operations.
Compression Ratio (CR), Data Encryption Standard (DES), Peak Signal-to-Noise Ratio (PSNR), Rivest Cipher 4(RC4), Rivest Cipher (RC2), Set Partitioning Hierarchical Trees (SPIHT)
- Quantin C, Fassa M, Coatrieux G, Breton V, Boire J-Y. Giving patients secure “google-like” access to their medical record. ICMCC Event 2008; London: United Kingdom; Ios Press; 2008 Jun. p. 1–8.
- Pan W, Coatrieux, NG, Cuppens-Boulahia F, Ch Roux C. Medical image integrity control combining digital signature and lossless watermarking. Data Privacy Management and Autonomous Spontaneous Security: 4th International Workshop, DPM 2009 and Second International Workshop, SETOP 2009; 2009. p. 153–62.
- Grgic S, Grgic M, Zovko-Cihlar B. Performance analysis of image compression using wavelets. IEEE Transactions on Industrial Electronics. 2001: 682–95.
- Said A, Pearlman WA. A new fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits & Systems for Video Technology. 1996 Jun; 6:243–50.
- Tahoces PG, Varela JR, Lado MJ, Souto M. Image compression: Maxshift ROI encoding options in JPEG2000. Computer Vision and Image Understanding. 2008 Feb; 109(2):139–45.
- Hang X, Greenberg NL, Thomas JD. Compression of pre-scan-converted echocardiographic video using wavelet packet and integer wavelet transforms. Image and Vision Computing. 2006 Sep; 24(9):915–25.
- Somasundaram K, Palaniappan N. Adaptive low bit rate facial feature enhanced residual image coding method using SPIHT for compressing personal ID images, AEU - International Journal of Electronics and Communications. 2011 Jun; 65(6):589–94.
- Lin Y-C, Varodayan D. Image authentication using distributed source coding. IEEE Transactions on Image Processing. 2012 Jan; 21(1):273–83.
- Johnson M, Ishwar P, Prabhakaran VM, Schonberg D, Ramchandran K. On compressing encrypted data. IEEE Transactions on Signal Processing. 2004 Oct; 52(10):2992–3006.
- Mohideen SK, Perumal A, Sathik, SM. Image de-noising using discrete wavelet transforms. International Journal of Computer Science and Network Security. 2008 Jan; 8(1):213–16.
- Caglar M. Long-range dependent workload model for packet data traffic. Mathematics of Operations Research. 2004; 29:92–105.
- Mantin I, Shamir A. A practical attack on broadcast RC4. Proceedings of Fast Software Encryption; 2001. p. 152–64.
- Chong C-Y, Kumar SP. Sensor networks: evolution, opportunities, and challenges. Proceedings of the IEEE. 2003 Aug; 91(8):1247–56.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.