Cover page and Table of Contents: PDF (size: 283KB)
Full Text (PDF, 283KB), PP.13-19
Views: 0 Downloads: 0
Compression Ratio, Shape - Adaptive Wavelet Transform, Scaling based ROI, JPEG2000 Max – Shift ROI Coding, JPEG2000, DCT
Medical image compression plays a key role as hospitals move towards filmless imaging and go completely digital. Image compression will allow Picture Archiving and Communication Systems (PACS) to reduce the file sizes on their storage requirements while maintaining relevant diagnostic information. Lossy compression schemes are not used in medical image compression due to possible loss of useful clinical information and as operations like enhancement may lead to further degradations in the lossy compression. Medical imaging poses the great challenge of having compression algorithms that reduce the loss of fidelity as much as possible so as not to contribute to diagnostic errors and yet have high compression rates for reduced storage and transmission time. This paper outlines the comparison of compression methods such as Shape-Adaptive Wavelet Transform and Scaling Based ROI,JPEG2000 Max-Shift ROI Coding, JPEG2000 Scaling-Based ROI Coding, Discrete Cosine Transform, Discrete Wavelet Transform and Subband Block Hierarchical Partitioning on the basis of compression ratio and compression quality.
S.Sridevi M.E, V.R.Vijayakuymar, R.Anuja, "A Survey on Various Compression Methods for Medical Images", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.3, pp.13-19, 2012. DOI:10.5815/ijisa.2012.03.02
I.Ueno and W.Pearlman, “Region of interest coding in volumetric images with shape-adaptive wavelet transform”, in Proc. SPIE, 2003, vol.5022.
C.Doukas and I.Maglogiannis, “Region of interest coding techniques for medical image compression”, IEEE Eng. Med. Biol. Mag.,vol.25, no.5, Sep-Oct.2007.
K.Krishnan, M.Marcellin, A.Bilgin, and M.Nadar, “Efficient transmission of comressed data for remote volume visualization”, IEEE Trans. Med. Imag., vol.25, no.9, Sep.2006.
R. Srikanth and A. G. Ramakrishnan, “Contextual encoding in uniform and adaptive mesh-based lossless compression of MR images,” IEEE Trans. Med. Imag., vol. 24, no. 9, Sep. 2005.
Y.Liu and W.A.Pearlman,” Resolution Scalable Coding and Region of Interest Access with Three-Dimensional SBHP Algorithm”, Third International symposium on 3D Data Processing, Jun 2006.
Ram Singh, Ramesh Verma and Sushil Kumar “JPEG2000: Wavelet Based Image Compression” EE678 wavelets application.
P. Schelkens, A. Munteanu, J. Barbarien, M. Galca, X. Giro-Nieto, and J. Cornelis, “Wavelet coding of volumetric medical datasets,” IEEE Trans. Med. Imag., vol. 22, no. 3, pp. 441–458, Mar. 2003.
K. Dezhgosha, A.K. Sylla, E. Ngouyassa, “Lossless and Lossy Image Compression Algorithms for On-board Processing in Spacecrafts” 1994 IEEE
Stelios C. Orphanoudakis, “Supercomputing in Medical Imaging” IEEE Engineering in Medicine and Biology Magazine December 1988
Zixiang Xiong, Xiaolin Wu, Samuel Cheng and Jianping Hua, “Lossy-to-Lossless Compression of Medical Volumetric Data Using Three-Dimensional Integer Wavelet Transforms” IEEE Trans. Med. Imag., vol. 22, no. 3, Mar. 2003.
Karen L. Gray “The JPEG2000 Standard”
Gloria Menegaz and Jean-Philippe Thiran, “Three Dimensional Encoding/Two-Dimensional Decoding of Medical Data”, IEEE Trans. Med. Imag., vol.22, no.3, Mar 2003.
Nikolay Ponomarenko, Vladimir Lukin, Karen Egiazarian, Jaakko Astola, “DCT Based High Quality Image Compression”.
Matthew J. Zukoski, Terrance Boult, Tunç Iyriboz, “A novel approach to medical image compression”, Int. J. Bioinformatics Research and Applications, Vol. 2, No. 1, 2006.
Salih Burak Gokturk, Carlo Tomasi, Bernd Girod, Chris Beaulieu, “Medical image compression based on region of interest, with application to colon CT images”.