NDLI: Medical Volume Noise Reduction Employing a Laplace Distribution with Local Variance for Modeling Contourlet Coefficients

Content Provider	IEEE Xplore Digital Library
Author	Rabbani, H. Vafadust, M. Gazor, S.
Copyright Year	2007
Description	Author affiliation: Amirkabir Univ. of Technol., Tehran (Rabbani, H.; Vafadust, M.)
Abstract	In this paper we introduce a simple shrinkage function employing local Laplace distribution for medical volume noise reduction in contourlet transform domain. Since we implement our denoising algorithm in contourlet domain, we are able to preserve the important details of noise-free images that for medical volume may contain important diagnostic information. It is clear that using maximum a posteriori (MAP) estimator for denoising problem, needs a prior distribution for noise-free data. In this paper we propose a Laplace probability density function (pdf) to model the statistical properties of contourlet coefficients. This distribution is able to simultaneously model the heavy-tailed nature and spatially clustering property of coefficients. We use the produced thresholding function from MAP estimator for denoising of a sequence of CT images corrupted with additive Gaussian noise in various noise levels. The simulation results show that our method has better performance visually and in terms of peek signal-to-noise ratio (PSNR) in comparison with several denoising methods.
Starting Page	107
Ending Page	110
File Size	2778796
Page Count	4
File Format	PDF
ISBN	9781424413454
DOI	10.1109/ISSMDBS.2007.4338304
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-19
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise reduction Biomedical imaging Medical diagnostic imaging Probability density function Computed tomography Additive noise Gaussian noise Noise level Signal to noise ratio PSNR
Content Type	Text
Resource Type	Article

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