Magnetic resonance image denoising using nonlocal maximum likelihood paradigm in DCT‐framework |
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| Authors: | P. Krishna Kumar P. Darshan Sheethal Kumar Rahul Ravindra Jeny Rajan Luca Saba Jasjit S. Suri |
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| Affiliation: | 1. Department of Computer Science and Engineering, National Institute of Technology Karnataka, India;2. Department of Radiology, University of Cagliari, Cagliari, Italy;3. Diagnostic and Monitoring Department, AtheroPoint LLC, Roseville, CA;4. Department of Electrical Engineering, Idaho State University (Aff.), Pocatello, ID;5. Global Biomedical Technologies Inc., Roseville, CA |
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| Abstract: | The data acquired by magnetic resonance (MR) imaging system are inherently degraded by noise that has its origin in the thermal Brownian motion of electrons. Denoising can enhance the quality (by improving the SNR) of the acquired MR image, which is important for both visual analysis and other post processing operations. Recent works on maximum likelihood (ML) based denoising shows that ML methods are very effective in denoising MR images and has an edge over the other state‐of‐the‐art methods for MRI denoising. Among the ML based approaches, the Nonlocal maximum likelihood (NLML) method is commonly used. In the conventional NLML method, the samples for the ML estimation of the unknown true pixel are chosen in a nonlocal fashion based on the intensity similarity of the pixel neighborhoods. Euclidean distance is generally used to measure this similarity. It has been recently shown that computing similarity measure is more robust in discrete cosine transform (DCT) subspace, compared with Euclidean image subspace. Motivated by this observation, we integrated DCT into NLML to produce an improved MRI filtration process. Other than improving the SNR, the time complexity of the conventional NLML can also be significantly reduced through the proposed approach. On synthetic MR brain image, an average improvement of 5% in PSNR and 86%reduction in execution time is achieved with a search window size of 91 × 91 after incorporating the improvements in the existing NLML method. On an experimental kiwi fruit image an improvement of 10% in PSNR is achieved. We did experiments on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 256–264, 2015 |
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| Keywords: | MRI noise denoising NLML discrete cosine transform |
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