| Paper title: |
A New Application of MSPIHT for Medical Imaging |
| Published in: | Issue 2, (Vol. 6) / 2012 |
| Publishing date: | 2011-10-24 |
| Pages: | 13-18 |
| Author(s): | ZITOUNI A., BAARIR Z., OUAFI A., TALEB AHMED Abdlmalik, TALEB AHMED Abdlmalik |
| Abstract. | In this paper, we propose a new application for medical imaging to image compression based on the principle of Set Partitioning In Hierarchical Tree algorithm (SPIHT). Our approach called , the modified SPIHT (MSPIHT), distributes entropy differently than SPIHT and also optimizes the coding. This approach can produce results that are a significant improvement on the Peak Signal-to-Noise Ratio (PSNR) and compression ratio obtained by SPIHT algorithm, without affecting the computing time. These results are also comparable with those obtained using the Set Partitioning In Hierarchical Tree (SPIHT) and Joint Photographic Experts Group 2000 (JPG2) algorithms. |
| Keywords: | Image Compression, SPIHT, MSPIHT, Entropy, Coding, PSNR, Compression Ratio. |
| References: | 1. R.Viswanath, Reddy1, T.Sreenivasulu Reddy2, and Dr.Govind Sharma31,3 IIT Kanpur, Dept. of EEE, Kanpur, Indi, “Efficient Coding of Image Subbands using Blockbased Modified SPIHT”, International Journal of Recent Trends in Engineering, Vol 2, N° 5, November 2009. 2. Nicola Sprljana,Sonja Grgicb,Mislay Grgicb, “Modified Spiht algorithm for wevlet packet image coding”, real-time Imaging 11 (2005) 378-388. 3. R.Suudhakar, R.Karthing, and S.Jayaraman, “Fingerprint Compression Using Contourlet Transform with Modified SPIHT Algorithm”. Iranian journal of electrical and Computer Engineering Vol 5,NO,1,Winter-spring 2006. 4. A.Said and W.A.Pearlman,”A new,fast and efficient image cdec based on set partitioning in hierarchical trees”,IEEE trans.circuits syst.video technol.6,243-250 (jun 1996) 5. J.M. Shapiro, “Embedded image coding using zerotres of wavelet coefficients,” IEEE Trans. on Signal Processing, vol. 41, pp. 3445–3462, Dec. 1993. 6. M. D. Adams, A. Antoniou, “Reversible EZW-Based Image Compression Using Best-Transform Selection and Selective Partial Embedding”. IEEE Transactions on circuits and systems-II: Analog and digital processing, Vol. 47, NO. 10, pp. 1119-1122, Oct. 2000. 7. ISO/ IEC CD 15444-1,ITU-T Rec T,800 CD VI .O (Committee Draft International Standard),”Information technologyJPEG2000 Image Coding System”,(December 1999). 8. M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies. “Image coding using wavelet transform”. IEEE Transaction, Image Processing, 1(2):205–220, April 1992. 9. A.Cohen,I.Daubechies,et J.feauveau. ”Biorthogonal bases of compactly supported wavelets”.communications on Pure and Applied Mathematics,45/485-560,1992. 10. M. Angeles Losada, Gulay Tohumoglu, David Fraile, Antonio Artes, “Multi-itération wavelet zerotree coding for image compression”. Elsevier Science, Signal Processing, vol. 80, pp. 1281-1287, 2000. 11. G. Donovan, J. Geronimo, D. Hardin, P. Massopust, “Construction of orthogonal wavelets using fractal interpolation functions”, SIAM J. Math. Anal., vol. 27, N_4, pp. 1158-1192, 1996. 12. B. Jawerth, W. Sweldens, “An overview of wavelet based multiresolution analyses”, web site, www.mathsoft.com/wavelets.html, pp. 1-39, 1997. 13. R. Thomas, “Algorithmes Rapides de Reconstruction en Tomographie par Compression des Calculs. Application a la Tomofluoroscopie 3D”. These de Doctorat, Institut national polytechnique de GRENOBLE, Octobre 2002. 14. Y.Low, , R.Besa, ”Wavelet-based medical image compression using EZW”. 4th National Conference on Telecommunication Technology Proceedings, Malaysia (2003). |
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