Wavelet-Based Medical Image Denoising and Enhancement

Article Preview

Abstract:

The visual quality of medical images is an important aspect in PACS implementation. In this study, on the basis of wavelet analysis, a denoising and enhancement algorithm for medical image is proposed. The algorithm mainly includes six steps. At first, an effcient method is investigated for Poisson Noise remove. Second, diagnosis features of the denoised image are enhanced by compressing the dynamic range. Third, we extract the high frequency component of the original image by the designed lowpass filter. Fourth, the extracted high frequency component are segment into diagnosis feature component in the high signal range, the diagnosis feature component in the low signal range, and the noise component. Five, we reconstruct an image using image fusion. Finally, we make DICOM calibration for used display and decide parameters of the image fusion, resulting in the diagnosis image. Experimental results show that this new scheme offers effective noise removal in medical images and enhancing sharpness. More importantly, this scheme can improve the diagnostic value of the display image on the commercial display successfully.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

515-520

Citation:

Online since:

August 2012

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Y. Xue, H. Liang. Understanding PACS Development in Context: The Case of China., IEEE Trans., Info Tech Biomed., VOL. 11, NO. 1, 2007, pp.14-16.

DOI: 10.1109/titb.2006.879580

Google Scholar

[2] H. Jiang, Y. Lu and L. Ma. A Novel Image Enhancement Technique for CR Chest Images, 2010 RISP International Workshop on Nonlinear Circuits and Signal Processing, Hawaii, USA, March 3-5, 2010, pp.239-242.

Google Scholar

[3] S. Mallat, A Wavelet Tour of Signal Processing. Academic Press, London, (1998).

Google Scholar

[4] D. L. Donoho, De-noising by soft-thresholding, IEEE Transactions on Information Theory, vol. 41, no. 3, 1995, p.613–627.

DOI: 10.1109/18.382009

Google Scholar

[5] Miller, M.; Kingsbury, N. Image denoising using derotated complex wavelet coefficients, IEEE Transactions on Image Processing, Vol. 17, pp.1500-1511, (2008).

DOI: 10.1109/tip.2008.926146

Google Scholar