• IEEE Trans Med Imaging · Sep 2003

    Comparative Study

    Noise reduction for magnetic resonance images via adaptive multiscale products thresholding.

    • Paul Bao and Lei Zhang.
    • Department of Information Engineering, The Chinese University of Hong Kong, Shatin, New Territory, Hong Kong. paulbao@ie.cuhk.edu.hk
    • IEEE Trans Med Imaging. 2003 Sep 1; 22 (9): 1089-99.

    AbstractEdge-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.

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