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Wavelet domain non-linear filtering for MRI denoising
Authors:C Shyam Anand  Jyotinder S Sahambi
Institution:1. Sherbrooke Connectivity Imaging Laboratory, Computer Science Department, Université de Sherbrooke, Sherbrooke, Canada;2. Unité Mixte de Recherche CNRS (UMR 5800), Laboratoire Bordelais de Recherche en Informatique, Bordeaux, France;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. iMinds Vision Lab (Dept. of Physics), University of Antwerp, Antwerp, Belgium;2. Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, NY, USA;3. ESAT/PSI, Department of Electrical Engineering, KU Leuven, Leuven, Belgium;1. College of Computer Science, Sichuan University, Chengdu 610065, China;2. Department of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China;3. Lab of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing 210096, China;4. School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China;5. Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210096, China;6. Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China;7. Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
Abstract:Feature-preserved denoising is of great interest in medical image processing. This article presents a wavelet-based bilateral filtering scheme for noise reduction in magnetic resonance images. Undecimated wavelet transform is employed to provide effective representation of the noisy coefficients. Bilateral filtering of the approximate coefficients improves the denoising efficiency and effectively preserves the edge features. Denoising is done in the square magnitude domain, where the noise tends to be signal independent and is additive. The proposed method has been adapted specifically to Rician noise. The visual and the diagnostic quality of the denoised image is well preserved. The quantitative and the qualitative measures used as the quality metrics demonstrate the ability of the proposed method for noise suppression.
Keywords:
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