Robust Rician noise estimation for MR images |
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| Authors: | Pierrick Coupé José V. Manjón Elias Gedamu Douglas Arnold Montserrat Robles D. Louis Collins |
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| Affiliation: | 1. Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Israel;2. The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Israel;3. Department of Physiology and Cell Biology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Israel;4. Departments of Medical Neuroscience and Brain Repair Centre, Dalhousie University, Canada;1. Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, P.R. China;2. Research Center for Medical Image Computing, The Chinese University of Hong Kong Shatin, New Territories, Hong Kong SAR, P.R. China;3. Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, P.R. China;4. Department of Biomedical Engineering and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, P.R. China;5. CUHK Shenzhen Research Institute, Shenzhen, Guangdong, P.R. China;6. Institute of Clinical Anatomy, Southern Medical University, Guangzhou, Guangdong, P.R. China;1. Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, USA;2. Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea;3. Data Processing Center, Northwestern Polytechnical University, Xi’an, China |
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| Abstract: | In this paper, a new object-based method to estimate noise in magnitude MR images is proposed. The main advantage of this object-based method is its robustness to background artefacts such as ghosting. The proposed method is based on the adaptation of the Median Absolute Deviation (MAD) estimator in the wavelet domain for Rician noise. The MAD is a robust and efficient estimator initially proposed to estimate Gaussian noise. In this work, the adaptation of MAD operator for Rician noise is performed by using only the wavelet coefficients corresponding to the object and by correcting the estimation with an iterative scheme based on the SNR of the image. During the evaluation, a comparison of the proposed method with several state-of-the-art methods is performed. A quantitative validation on synthetic phantom with and without artefacts is presented. A new validation framework is proposed to perform quantitative validation on real data. The impact of the accuracy of noise estimation on the performance of a denoising filter is also studied. The results obtained on synthetic images show the accuracy and the robustness of the proposed method. Within the validation on real data, the proposed method obtained very competitive results compared to the methods under study. |
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