Noise reduction for magnetic resonance images via adaptive multiscale products thresholding

Edge-preserving denoising is of great interest in medical image processing. This paper presents a wavelet-based multiscale products thresholding scheme for noise suppression of magnetic resonance images. A Canny edge detector-like dyadic wavelet transform is employed. This results in the significant...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2003-09, Vol.22 (9), p.1089-1099
Main Authors: Bao, Paul, Zhang, Lei
Format: Article
Language:English
Subjects:
Additive white noise
Algorithms
Biological and medical sciences
Feedback
Gaussian noise
Humans
Image edge detection
Image Enhancement - methods
Liver - anatomy & histology
Magnetic noise
Magnetic resonance
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Medical sciences
Multivariate Analysis
Noise reduction
Rician channels
Signal Processing, Computer-Assisted
Signal to noise ratio
Spine - anatomy & histology
Stochastic Processes
Wavelet transforms
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Summary:Edge-preserving denoising is of great interest in medical image processing. This paper presents a wavelet-based multiscale products thresholding scheme for noise suppression of magnetic resonance images. A Canny edge detector-like dyadic wavelet transform is employed. This results in the significant features in images evolving with high magnitude across wavelet scales, while noise decays rapidly. To exploit the wavelet interscale dependencies we multiply the adjacent wavelet subbands to enhance edge structures while weakening noise. In the multiscale products, edges can be effectively distinguished from noise. Thereafter, an adaptive threshold is calculated and imposed on the products, instead of on the wavelet coefficients, to identify important features. Experiments show that the proposed scheme better suppresses noise and preserves edges than other wavelet-thresholding denoising methods.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2003.816958