Medical Image Condensation Based on Wavelet Transform

Article Preview

Abstract:

This paper based on the peculiarity of wavelet transform that its transform only in vacuum region and frequency region, decompose image use the theory of wavelet, obtain a series sub-image of different resolution ratio. The value of higeresolution ratio sub-image is all verge on 0, the phenomenon is more obviously in high frequency, so that, the mainly proportion is low frequency to a image. Use wavelet decomposition get rid of the high frequency, only reservation low frequency, to realize the aim of condensation image. Through the simulation of contradistinctive image of cerebra framework remotion between three-dimensional ultrasonic imaging in course of OPS and MIR preceding OPS validated the feasibility by Matlab.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

978-981

Citation:

Online since:

January 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Ramin Eslami, Hayder Radha. A new family of nonredundant transforms using hybrid wavelets and directional filter banks [J]. In IEEE Transactions on image processing, 2007, 16(4): 1152-1167.

DOI: 10.1109/tip.2007.891791

Google Scholar

[2] Eslami.R. and Radha.H., Regular hybrid wavelets and directional filter banks: extensions and applications [C]. IEEE International Conference on Image Processing, 2006: 1609-1612.

DOI: 10.1109/icip.2006.312617

Google Scholar

[3] Nie sheng-dong, Li Lihong, Chen Zhaoxue. A XI feature-based pulmonary nodule segmentation using three-domain mean shift clustering [J]. Journal of East China Normal University. 2008(1): 60-67.

DOI: 10.1109/icwapr.2007.4420699

Google Scholar

[4] Shuqian Luo and Guohong Zhou, Medical Image Processing and Analyse. [M]. Publishing House of Science in Beijing, (2010).

Google Scholar

[5] Jamshid D, Hamdan A, Manlio V, et al. Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach [J]. IEEE Trans on Med Imaging, 2008, 27(4): 467-480.

DOI: 10.1109/tmi.2007.907555

Google Scholar

[6] Geromel, J. C., de Oliveira, M. C., Bernussou, J. Robust filtering of discrete-time linear systems with parameter dependent Lyapunov functions [J]. SIAM Journal on Control Optimization, 2002, 41(3): 700–711.

DOI: 10.1137/s0363012999366308

Google Scholar