NDLI: A comparative study on mammographic image denoising technique using wavelet, curvelet and contourlet transforms

Content Provider	IEEE Xplore Digital Library
Author	Malar, E. Kandaswamy, A. Kirthana, S.S. Nivedhitha, D.
Copyright Year	2012
Description	Author affiliation: Department of Biomedical Engineering, PSG College of Technology, Coimbatore, India (Malar, E.; Kandaswamy, A.; Kirthana, S.S.; Nivedhitha, D.)
Abstract	This article focuses on comparing the discriminating power of the various multi-resolution based thresholding techniques - wavelet, curvelet, and contourlet for image denoising. Using multiresolution techniques, mammogram images are decomposed into different resolution levels, which are sensitive to different frequency bands. We implement the proposed algorithm on the mammogram images embedded in Random, Salt and Pepper, Poisson, Speckle and Gaussian noises. Curvelet transform employed in the proposed scheme provides sparse decomposition as compared to the wavelet and contourlet transform methods. The curvelet transform has a strong directional character which combines multiscale analysis and ideas of geometry to achieve the optimal rate of convergence by simple thresholding. The proposed algorithm succeeded in providing improved denoising performance to recover the shape of edges and important detailed components. Empirical results proved that the curvelet-based thresholding can obtain a better image estimate than the wavelet- based and contourlet-based restoration methods.
Starting Page	65
Ending Page	68
File Size	546249
Page Count	4
File Format	PDF
ISBN	9781467323192
e-ISBN	9781467323222
DOI	10.1109/MVIP.2012.6428762
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-14
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms denoising Wavelet Curvelet Noise reduction Contourlet Image restoration Image denoising Signal to noise ratio
Content Type	Text
Resource Type	Article

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