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

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.

[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.