A Survey on Medical Image Compression Techniques
S.Sridevi1, V.R.Vijayakumar2, R.Anuja3
1Mrs.S..Sridevi Studied her B.E. (CSE) from Thiagarajar College of Engineering, and her M.E. (CSE) from RVS College of Engg. And Tech., Dindugul. Currently she is Pursuing her Ph.D. in the area of Image Processsing in Anna University of Technology, Coimbatore. Currently she is working as an Associate Professor in the Department of Computer Science and Engineering in Sethu Institute of Technology, Virudhunagar Dist. She has Attended National and International conference Proceedings.
2Dr.V.R.Vijaya kumar,M.E.,Ph.D, currently working as a Prof & Head of Department of Electronics and Communication Engineering, Anna University of Technology, Coimbatore. He has Published Number of Research papers in National and International Journals and Conferences. He has delivered many guest lectures on digital image processing and digital signal processing in many colleges. His area of interest are digital image processing, computer networks etc.
3Ms. R.Anuja is pursuing her M.E (Computer Science and Engineering) in Sethu Institute of Technology, Virudhunagar. Her research area includes Image processing. She has presented a paper in International Conference.
Manuscript received on January 17, 2012. | Revised Manuscript received on February 05, 2012. | Manuscript published on February 29, 2012. | PP: 228-232 | Volume-1 Issue-3, February 2012. | Retrieval Number: B0148121211/2011©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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 Wavelet Transform and Subband Block Hierarchical Partitioning on the basis of compression ratio and compression quality.
Keywords: Lossy Compression Ratio, Shape – Adaptive Wavelet Transform, Scaling based ROI, JPEG2000 Max – Shift ROI Coding, JPEG2000, DCT.