Total views : 1066
3D Medical Image Compression: A Review
In this paper a comprehensive survey of the state of the art lossy and lossless techniques available in the literature has been presented and the merits and pitfalls of each technique are analyzed. This study congregates the pioneer works in two dimensional (2D) compression techniques, both in pixel domain and transform domain. The evolution of compression of three dimensional (3D) medical images from 2D compression has also been discussed. Compressed medical image has to be both diagnostically lossless and less bandwidth in addition to visual quality. Region of Interest coding (ROI) which achieves diagnostic quality image with less bandwidth has been explored. In spite of proven compression technologies, only the lossless compression has been used widely around the world and the reason for the same has been investigated. In addition it also investigates several factors; why one needs to go for lossy compression.
3D Medical Image, Context based Boding, DCT, DWT, Predictive Coding, VOI
- Kwong S, Ho Y. A statistical Lempel-Ziv compression algorithm for Personal Digital Assistant (PDA). IEEE T Consum Electron. 2001; 47(1):154–62
- Boulgouris N, Tzovaras D, Strintzis M. Lossless image compression based on optimal prediction, adaptive lifting, and conditional arithmetic coding. IEEE T Image Processing.2001; 10(1):1–14.
- Wu X, Menon N. Context-based adaptive lossless image coding. IEEE T Communications. 1997: 45:437–44.
- Gaudeau Y, Moureaux JM. Lossy compression of volumetric medical images with 3D dead zone lattice vector quantization. ANN Telecommunication. 2009; 64(5-6):359–67.
- Rao KR, Yip PC. Transforms and Data Compression. CRC press book; 2000.
- Benoit-Cattin H, Baskurt A, Prost R. 3D medical image coding using separable 3D wavelet decomposition and lattice vector quantization. Signal Processing. 1997; 59:139–53.
- Dandawate YH, Jadhav TR, Chitre A, Joshi MA. Neuro-wavelet based vector quantizer design for image compression. Indian Journal of Science and Technology.2009 Oct; 2(10):56–61.
- Vetterli M, Kovacevic J. Wavelets and subband coding. Englewood Cliffs: Prentice-Hall; 1995.
- Shapiro J. Embedded image coding using zerotrees of wavelet coefficients. IEEE T Signal Processing. 1993;41(12):3445–62.
- Taubman D. High performance scalable image compression with EBCOT. IEEE T Image Processing. 2000; 9(7):1158–70.
- Said A, Pearlman WA. A new fast and efficient image codec based on set partitioning in hierarchical trees. IEEE T Circuits and Systems for Video Technology. 1996; 6:243–50.
- Cho S, Kim D, Pearlman W. Lossless compression of volumetric medical images with improved three-dimensional SPIHT algorithm. J Digit Imaging. 2004; 17:57–63.
- Islam A, Pearlman WA. An embedded and efficient lowcomplexity hierarchical image coder. Proceedings of Visual Communications and Image Processing, SPIE; 1999. p.294–305.
- Bilgin A, Zweig G, Marcellin M. Three-dimensional image compression with integer wavelet transforms. Appl Optics.2000; 39:1799–814.
- Jiang W, Kiang SZ, Hakim NZ, Meadows HE. Lossless compressioncompression for medical imaging systems using linear/nonlinear prediction and arithmetic coding. Proceedings of International Symposium on Circuits and Systems; IEEE;1993. p. 283–6.
- Weinberger M, Seroussi G, Sapiro G. LOCO-I: a low complexity, context-based, lossless image compression algorithm. Proceedings of Data Compression Conference;IEEE; 1996. p. 140–9.
- Tai S, Wu Y, Lin CW. An adaptive 3D discrete cosine transform coder for medical image compression. IEEE T Information Technology in Biomedicine. 2000; 4(3):259–63.
- Sunder RS, Eswaran C, Sriraam N. Medical image compression using 3-D Hartley transforms. Comput Biol Med.2006; 36:958–73.
- Qi X, Tyler J. A progressive transmission capable diagnostically lossless compression scheme for 3D medical image sets. Information Sciences. 2005; 175:217–43.
- Loganathan R, Kumaraswamy YS. Active contour based medical image segmentation and compression using biorthogonal wavelet and embedded zerotree. Indian Journal of Science and Technology. 2013 Apr; 6(4):4390–5.
- Arumugadevi S, Seenivasagam V. Comparison of clustering methods for segmenting color images. Indian Journal of Science and Technology. 2015 Apr; 8(7):670–7.
- Smistad E, Falch T, Bozorgi M, Elster AC, Lindseth F. Medical image segmentation on GPUs – A comprehensive review. 2014 Dec; 20:1–18.
- Tian J, Wells RO, A lossy image codec based on index coding. Data Compression Conference; IEEE; 1996. p. 456–68.
- Tian J, Wells R. Embedded image coding using wavelet-difference-reduction. Wavelet Image and Video Compression. Norwell: Kluwer Academic Publications; 1998. p. 289–301.
- Liu Y, Pearlman WA. Region of interest access with three dimensional SBHP algorithms. Proceedings of SPIE; SPIE;2006. p. 17–9.
- Sanchez V, Abu-Gharbieh R, Nasiopoulos P. 3-D scalable medical image compression with optimized volume of interest coding. IEEE T Medical Imaging. 2010; 29(10):1808–20.
- Gokturk SB, Tomasi C, Girod B, Beaulieu C. Medical image compression based on region of interest with application to colon CT images. Proceedings of 23rd Annual International Conference IEEE. 2001; 3:2453–6.
- Sanchez V, Abu-Gharbieh R, Nasiopoulos P. Symmetrybased scalable lossless compression of 3D medical image data. IEEE T Medical Imaging. 2009; 28(7):1062–72.
- Yang W, Zhang S, Chen Y, Li W, Chen Y. Shape symmetry analysis of breast tumors on ultrasound images. Comput Biol Med. 2009; 39(3):231–8.
- Yina XX, Ng BW, Ramamohanarao K, Abbott D. Tensor based sparse decomposition of 3D shape for visual detection of mirror symmetry. Comput Meth Prog Bio. 2012;108:629–43.
- Munoz-Gomez J, Bartrina-Rapesta J, Marcellin MW, Serra-Sagrist J. Correlation Modeling for Compression of Computed Tomography Images. IEEE J Biomedicine and Health Informatics. 2013; 17:5.
- American College of Radiology. ACR technical standard for electronic practice of medical imaging. ACR Practice Guideline. Reston VA: 2007.
- Razaak M, Martini MG, Savino K. A study on quality assessment for medical ultrasound video compressed via HEVC. IEEE Journal of biomedical and health informatics.2015 Sep; 18(5):1552–9.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.