Scintigraphic Image Coding of Region of Interest Based On SPIHT Algorithm Using Global Thresholding and Huffman Coding
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Scintigraphic Image Coding of Region of Interest Based On SPIHT Algorithm Using Global Thresholding and Huffman Coding

Authors: A. Seddiki, M. Djebbouri, D. Guerchi

Abstract:

Medical imaging produces human body pictures in digital form. Since these imaging techniques produce prohibitive amounts of data, compression is necessary for storage and communication purposes. Many current compression schemes provide a very high compression rate but with considerable loss of quality. On the other hand, in some areas in medicine, it may be sufficient to maintain high image quality only in region of interest (ROI). This paper discusses a contribution to the lossless compression in the region of interest of Scintigraphic images based on SPIHT algorithm and global transform thresholding using Huffman coding.

Keywords: Global Thresholding Transform, Huffman Coding, Region of Interest, SPIHT Coding, Scintigraphic images.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1097301

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[1] Z. Xiang, K. Ramachandran, M. T. Orchard and Y. Q. Zhing, A comparative study of DCT and Wavelet based image coding, IEEE Transaction on Circuits Systems Video Technology, vol. 9, April 1999
[2] R. Sudhakar, M. R. Karthiga and S. Jayaraman, Image Compression using Coding of Wavelet Coefficients-A Survey, ICGST-International Journal on Graphics, Vision and Image processing (GVIP), vol. 5, pp. 25-38, 2005.
[3] F. Douak, R. Benzidi, and N. Benoudjit, Color image compression algorithm based on the DCT transform combined to an adaptative block scanning, AEU-International Journal of Electronics and Communication, vol. 65, pp 16-26, Jan. 2011
[4] B.K.T. Ho, M.-J. Tsai, J. Wei, M. Ma, and P. Saipetch, Video compression of coronary angiograms based on discrete wavelet transform with block classification, IEEE Transactions on Medical Imaging, Dec. 1996.
[5] M. D.Adams, and F. Kossentini, Performance Evaluation of reversible integer to integer Wavelet Transforms for Image compression, IEEE Trans on image Processing, vol. 9, pp1010-1024, June 2000.H. Poor, An Introduction to Signal Detection and Estimation. New York: Springer- Verlag, 1985, ch. 4.
[6] J. Wang, F. Zhang, Study of the image compression based on SPIHT algorithm, IEEE International Conference on Intelligent Computing and cognitive Informatics(ICICCI), pp. 130-133, 2010.
[7] H. Zhu, C. Xiu, and D. Yang, An improvement SPIHT algorithm based on Wavelet coefficient blocks for image coding, IEEE International Conference on Computer Application and System Modeling (ICCASM), vol. 2, pp. 646-649, 2010.
[8] C. Xiu and H. Zhu, A modified SPIHT algorithm based on Coefficient blocks for Robust Image Transmission over Noisy Channel, IEEE International Symposium on information Science and Engeneering (ISISE), pp. 58-61, 2010.
[9] U. Qidawai, C. H. Chen, Digital Image Processing: An Algorithmic Approach with Matlab, CRC press, 2009.
[10] A. Said, W. A. Pearlman, A new, Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees, IEEE Transactions on Circuits and Systems for Video Technology, vol.6, pp. 1-16, 1996.
[11] B. Chandra, B. Chanda, Color image compression based on block truncation coding using pattern fitting principle, Pattern Recognition, vol. 40, pp. 2408-2417, Sept. 2007.