Comparing application of the wavelet transform and the adaptive weight smoothing algorithm for the noise reduction in magnetic resonance imaging

• Autorzy: Świebocka–Więk J. , Figiel H.
• Języki publikacji: EN

Abstrakty

EN

The analysis of the Adaptive Weight Smoothing (AWS) and the Discrete Wavelet Transform (DWT) application in the MRI images improvement is presented. The results show how the AWS and the DWT algorithms can be used for the noise reduction in the MRI. The DWT application gives much better visual results without blurring. It is shown that for the DWT a bigger number of the input parameters (like the wavelet’s choice, number of the iteration, degree of the decomposition, kind of the smoothing thresholding etc.) is needed, what makes it more difficult to optimize the output image. The analysis of the Signal to Noise Ratio (SNR) and the Contrast to Noise Ratio (CNR) and edge quality detection for both methods is presented to show their effectiveness. We concluded that the AWS algorithm can be applied to improve MRI images mainly in the case of high noise and low Signal to Noise Ratio (SNR) whereas wavelet transform is effective in any case. The wavelet transform application provides additional possibilities like image compression and image fusion, which can be useful in the MRI.

Słowa kluczowe

EN

magnetic resonance imaging; noise reduction; edge detection; adaptive smoothing; wavelet transform

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2011
• Tom: Vol. 7, no. 2
• Strony: 55--66
• Opis fizyczny: Bibliogr. 6 poz., zdj., tab.

Twórcy

autor

Świebocka–Więk J.

• AGH–University of Science and Technology, Faculty of Physics and Applied Computer Science, Al. Mickiewicza 30, 30–059 Cracow, Poland

autor

Figiel H.

• AGH–University of Science and Technology, Faculty of Physics and Applied Computer Science, Al. Mickiewicza 30, 30–059 Cracow, Poland

Bibliografia

• 1. Polzehl J., Spokoiny V. G., Adaptive Weights Smoothing with applications to image restoration, J. R. Stat. Soc. Ser. B Stat. Methodology 2000, 62: 335-354.
• 2. Polzehl J., Spokoiny V. G., Propagation–Separation Approach for Local Likelihood Estimation. Probability Theory and Related Fields 2006, 135: 335-362.
• 3. Kolecki J., Badurska M. Despeckling of synthetic aperture radar images using propagation–separation approach for local likelihood estimation, Archives of Photogrammetry, Cartography and Remote Sensing 2008, 18a: 251-261.
• 4. Alexander M.E., Baumgarter R., Summers A.R, A Wavelet based method for improving signal–to–noise ratio and contrast in MR images, Magnetic Resonance Imaging 2000, 18: 169–180.
• 5. Kolecki J., Wrobel A. Edge detection using adaptive smoothing of digital images, Archives of Photogrammetry, Cartography and Remote Sensing 2008, 18a: 263-272.
• 6. Bialasiewicz T, Falki i aproksymacje, Wydawnictwo Naukowo-Techniczne: Warszawa 2000, pp. 204-218.