Please wait a minute...
Frontiers of Electrical and Electronic Engineering

ISSN 2095-2732

ISSN 2095-2740(Online)

CN 10-1028/TM

Frontiers of Electrical and Electronic Engineering in China  2009, Vol. 4 Issue (1): 1-4   https://doi.org/10.1007/s11460-009-0014-1
  RESEARCH ARTICLE 本期目录
Medical image lossless compression based on combining an integer wavelet transform with DPCM
Medical image lossless compression based on combining an integer wavelet transform with DPCM
Lihong ZHAO(), Yanan TIAN, Yonggang SHA, Jinghua LI
School of Information Science & Engineering, Northeastern University, Shenyang 110004, China.
 全文: PDF(131 KB)   HTML
Abstract

To improve the classical lossless compression of low efficiency, a method of image lossless compression with high efficiency is presented. Its theory and the algorithm implementation are introduced. The basic approach of medical image lossless compression is then briefly described. After analyzing and implementing differential plus code modulation (DPCM) in lossless compression, a new method of combining an integer wavelet transform with DPCM to compress medical images is discussed. The analysis and simulation results show that this new method is simpler and useful. Moreover, it has high compression ratio in medical image lossless compression.

Key wordsmedical image    integer wavelet transform    differential plus code modulation (DPCM)    lossless compression
出版日期: 2009-03-05
Corresponding Author(s): ZHAO Lihong,Email:zhaolihong@ise.neu.edu.cn   
 引用本文:   
. Medical image lossless compression based on combining an integer wavelet transform with DPCM[J]. Frontiers of Electrical and Electronic Engineering in China, 2009, 4(1): 1-4.
Lihong ZHAO, Yanan TIAN, Yonggang SHA, Jinghua LI. Medical image lossless compression based on combining an integer wavelet transform with DPCM. Front Elect Electr Eng Chin, 2009, 4(1): 1-4.
 链接本文:  
https://academic.hep.com.cn/fee/CN/10.1007/s11460-009-0014-1
https://academic.hep.com.cn/fee/CN/Y2009/V4/I1/1
Fig.1  
Fig.2  
imageentropycompression ratio
original imageDPCM predictionDPCM+ Huffman
Barbara7.475.655.672
Lena7.454.544.580
brain CT4.842.232.275
chest X ray6.463.753.820
Tab.1  
Fig.3  
Fig.4  
test imagesentropy/Shannon
original imagesDPCMIWTDPCM+ IWT
Barbara7.475.655.575.14
Lena7.454.544.424.42
brain CT4.842.232.531.99
chest X ray6.463.754.023.66
Tab.2  
test imagescompression ratio/bpp
HuffmanDPCM+ HuffmanIWT+ HuffmanDPCM+ IWT+ Huffman
Barbara7.4965.6825.5835.187
Lena7.4764.5804.4614.455
brain CT4.8812.2752.5902.007
chest X ray6.4983.8204.0593.738
Tab.3  
test imagesencoding time/sdecoding time/s
DPCMIWTDPCM+ IWTDPCMIWTDPCM+ IWT
Barbara0.410.540.630.240.340.38
Lena0.360.530.610.220.320.36
brain CT0.230.430.510.190.300.35
chest X ray0.340.510.560.200.330.36
Tab.4  
1 Xiao Z M. Image Information Theory and Compression Coding Technology. Guangzhou: Sun Yat-sen University Press , 2000, 103 –235 (in Chinese)
2 Ding G G, Ji W P, Guo B L. Visual C++6.0 Digital Image Coding. Beijing: China Machine Press, 2004, 10–24 (in Chinese)
3 Zhang H Y, Wang D M, Song K O, Guan B G. Image compression technology. Journal of System Simulation , 2002, 14(7): 831–835 (in Chinese)
4 Adams M D, Kossentni F. Reversible integer-to-integer wavelet transforms for image compression: performance evaluation and analysis. IEEE Transactions on Image Processing , 2000, 9(6): 1010–1024
doi: 10.1109/83.846244
5 Calderbank A R, Daubechies I, Sweldens W, Boon-Lock Y. Lossless image compression using integer to integer wavelet transforms. In: Proceedings of International Conference on Image Processing, Santa Barbara, CA . 1997, 1: 596–599
6 Abousleman G P, Marcellin M W, Hunt B R. Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT. IEEE Transactions on Geoscience & Remote Sensing , 1995, 33(1): 26–34
doi: 10.1109/36.368225
7 Mallat S G. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence , 1989, 11(7): 674–693
doi: 10.1109/34.192463
8 Shapiro J M. Embedded image coding using zero trees of wavelet coefficients. IEEE Transactions on Signal Processing , 1993, 41(12): 3445–3462
doi: 10.1109/78.258085
9 Mallat S G. Multifrequency channel decompositions of images and wavelet models. IEEE Transactions on Acoustics, Speech and Signal Processing , 1989, 37(12): 2091–2110
doi: 10.1109/29.45554
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed