DocumentCode :
176436
Title :
An improved MRI denoising algorithm based on wavelet shrinkage
Author :
Kaikai Song ; Qiang Ling ; Zhaohui Li ; Feng Li
Author_Institution :
Univ. of Sci. & Technol. of China, Hefei, China
fYear :
2014
fDate :
May 31 2014-June 2 2014
Firstpage :
2995
Lastpage :
2999
Abstract :
Magnetic resonance imaging (MRI) is very important in medical diagnosis. Denoising is a critical step for MRI diagnosis. Wavelet shrinkage is an efficient denoising method. It can be further classified into two types, the threshold method and the proportional-shrink method. However, both methods have their disadvantages. When the threshold method is implemented, the noise cannot be perfectly removed under a hard threshold while the denoised image may have fuzzy edges with a soft threshold. Furthermore, when the noise is too strong, the noise removal may not be enough by the threshold method. The proportional-shrink method requires that the variance field of the wavelet coefficients should change smoothly and the noise should obey a Gaussian distribution. If these assumptions are violated, the estimated ratios would not be precise so that too much texture information may be removed and the image can be distorted. This paper presents an improved method to combine the above two methods. By combining the processed results together, the improved method can achieve a good balance between denoising and retaining the texture information. We verify the efficiency of our method through some simulated data from an open database.
Keywords :
Gaussian distribution; biomedical MRI; edge detection; fuzzy set theory; image denoising; image texture; medical image processing; shrinkage; wavelet transforms; Gaussian distribution; MRI denoising algorithm; MRI diagnosis; denoised image; denoising method; estimated ratio; fuzzy edges; magnetic resonance imaging; medical diagnosis; noise removal; proportional-shrink method; texture information; threshold method; wavelet coefficient; wavelet shrinkage; Equations; Magnetic resonance imaging; Noise figure; Noise reduction; Rician channels; Signal to noise ratio; denoising; magnetic resonance imaging; proportional-shrink; threshold; wavelet shrinkage;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-3707-3
Type :
conf
DOI :
10.1109/CCDC.2014.6852687
Filename :
6852687
Link To Document :
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