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Proceedings Paper

Wavelet packet and neural network basis medical image compression
Author(s): Xiuying Zhao; Jingyuan Wei; Linpei Zhai
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Paper Abstract

It is difficult to get high compression ratio and good reconstructed image by conventional methods; we give a new method of compression on medical image. It is to decompose and reconstruct the medical image by wavelet packet. Before the construction the image, use neural network in place of other coding method to code the coefficients in the wavelet packet domain. By using the Kohonen's neural network algorithm, not only for its vector quantization feature, but also for its topological property. This property allows an increase of about 80% for the compression rate. Compared to the JPEG standard, this compression scheme shows better performances (in terms of PSNR) for compression rates higher than 30. This method can get big compression ratio and perfect PSNR. Results show that the image can be compressed greatly and the original image can be recovered well. In addition, the approach can be realized easily by hardware.

Paper Details

Date Published: 27 October 2006
PDF: 6 pages
Proc. SPIE 6047, Fourth International Conference on Photonics and Imaging in Biology and Medicine, 60471O (27 October 2006); doi: 10.1117/12.710973
Show Author Affiliations
Xiuying Zhao, Changchun Institute of Optics, Fine Mechanics and Physics (China)
Graduate School of the Chinese Academy of Science (China)
Aviation Univ. of Air Force (China)
Jingyuan Wei, Bai Qiuen Medicinal Ministry, Jilin Univ. (China)
Linpei Zhai, Changchun Institute of Optics, Fine Mechanics and Physics (China)


Published in SPIE Proceedings Vol. 6047:
Fourth International Conference on Photonics and Imaging in Biology and Medicine
Kexin Xu; Qingming Luo; Da Xing; Alexander V. Priezzhev; Valery V. Tuchin, Editor(s)

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