An Algorithm for Medical Imagining Compression That Is Oriented to ROI-Characteristics Protection

Renjun  Shuai; Yang  Shen; Jing  Pan

doi:10.4236/jamp.2015.37106

Renjun Shuai, Yang Shen, Jing Pan

Abstract: In order to protect the ROI (region of interest) characteristics while greatly improving medical imaging compression ratio, we are proposing an algorithm for medical imagining compression that is oriented to ROI-characteristics protection. Firstly, an improved ROI segmentation algorithm is put forward based on the analysis of the ROI segmentation. Then, after the ROI segmented, the ROI edge is extracted and encoded with Freeman chain coding. Finally, the ROI is compressed by lossless compression with shearlet; the ROB (region of background) is compressed by the method of high ratio lossy compression combining with Wavelet and Fractal. Simulation results show that the ROI is segmented precisely. It holds edge integrity and has high quality reconstruction processed by the presented method, helping protect ROI characteristics while greatly improving the compression ratio.

Keywords: Medical Imaging Compression, ROI Characteristics Protecting, Segmentation, Chain Coding, Shearlet

Cite this paper: Shuai, R. , Shen, Y. and Pan, J. (2015) An Algorithm for Medical Imagining Compression That Is Oriented to ROI-Characteristics Protection. Journal of Applied Mathematics and Physics, 3, 854-861. doi: 10.4236/jamp.2015.37106.

References

[1] Nister, D. and Christopoulos, C. (1998) Lossless Region of Interest with a Naturally Progressive Still Image Coding Algorithm. IEEE Transactions on Image Processing, 3, 856-860.
http://dx.doi.org/10.1109/icip.1998.727388

[2] Tahoces, P.G., Varela, J.R. and Ladoetal, M.J. (2008) Image Compression: Maxshift ROI Encoding Options in JPEG2000. Computer Vision and Image Understanding, 109, 139-145.
http://dx.doi.org/10.1016/j.cviu.2007.07.001

[3] Ameer, S. and Basir, O. (2009) Image Compression Using Plane Fitting with Inter-Block Prediction. Image and Vision Computing, 27, 385-390. http://dx.doi.org/10.1016/j.imavis.2008.06.005

[4] Li, P., Jiang, H.Q., Yang, X.P. and Liu, Y.M. (2013) Medical Image near Lossless Compression Applicable to PACS. China Image and Graphics, 18, 699-705.

[5] Itti, L. and Kouch, C. (2001) Computational Modeling of Visual Attention. Nature Reviews Neuroscience, 2, 194-230. http://dx.doi.org/10.1038/35058500

[6] Itti, L. and Kouch, C. (2001) Feature Combination Strategies for Saliency-Based Visual Attention Systems. Journal of Electronic Imaging, 10, 161-169. http://dx.doi.org/10.1117/1.1333677

[7] Yang, D.H. (2002) A New Image Compression Algorithm Based on Wavelet Transform and Human Visual System. Chongqing University, Chongqing.

[8] Yu, M. and Pi, Y.Q. (2012) A Extraction Method of the Region of Interest. Electronic Design Engineering, 20, 160-162.

[9] Liu, Y.K. (2001) The Study of Freeman Chain Code Compression Algorithm. Journal of Computers, 24, 1294-1298.

[10] Guo, Q. (2010) Research on Shearlet-Based Statistical Model for Images and Its Applications. Shanghai University, Shanghai.

[11] Wang, F.X. and Zhou, K. (2008) Huffman Coding to Achieve File Compression and Decompression. Wuhan Polytechnic University, 46-49.

[12] Hu, H. (2010) Research on Medical Image Compression Based on Wavelet Transform. Wuhan University of Technology, Wuhan.

[13] Tan, Y.S. and Zhou, X.M. (2003) A New Improved Algorithm of Fractal Image Compression. Electronics Technology, 11, 1739-1742.

[14] Yang, Q. and Wang, H.J. (2013) Medical Image Fractal Compression Method Study Based on Multi-Wavelet Transform. Science Technology and Engineering, 24, 1671-1815.

[15] Joan, B.R., Joan, S.S. and Francesc, A. (2009) JPEG2000 ROI Coding Method with Perfect Fine-Grain Accuracy and Lossless Recovery. Proceedings of the 43rd Asilomar Conference on Signals, Systems and Computers. Monterey, California, IEEE Signal Processing Society, USA, 558-562.