Image denoising for magnetic resonance imaging medical images using improved generalized cross‐validation based on the diffusivity function
Various image denoising algorithms have been developed for medical imaging. But some disadvantages have been found, including the block effect, which increases smoothing, and the loss of image detail. Using the statistical properties of observed noisy images, we propose a new diffusivity function‐ba...
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| Published in: | International journal of imaging systems and technology Vol. 32; no. 4; pp. 1263 - 1285 |
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| Main Authors: | , , , , , , |
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Hoboken, USA
John Wiley & Sons, Inc
01.07.2022
Wiley Subscription Services, Inc |
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| ISSN: | 0899-9457, 1098-1098 |
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| Abstract | Various image denoising algorithms have been developed for medical imaging. But some disadvantages have been found, including the block effect, which increases smoothing, and the loss of image detail. Using the statistical properties of observed noisy images, we propose a new diffusivity function‐based partial differential equation method for image denoising. This model incorporates a Quaternion Wavelet Transform for the generation of the various noisy image coefficients, an improved generalized cross‐validation function for generating the optimal threshold value via the soft threshold function, and a new diffusivity function for controlling the diffusion process. The fourth‐order PDE diffusivity function, which is presented in this article, is a novel diffusion coefficient that is more effective at removing noise and preserving edges than previous approaches. Finally, the performance of the proposed method is evaluated using the peak signal‐to‐noise ratio, mean square error, structural similarity index, and comparisons to other traditional methods. |
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| AbstractList | Various image denoising algorithms have been developed for medical imaging. But some disadvantages have been found, including the block effect, which increases smoothing, and the loss of image detail. Using the statistical properties of observed noisy images, we propose a new diffusivity function‐based partial differential equation method for image denoising. This model incorporates a Quaternion Wavelet Transform for the generation of the various noisy image coefficients, an improved generalized cross‐validation function for generating the optimal threshold value via the soft threshold function, and a new diffusivity function for controlling the diffusion process. The fourth‐order PDE diffusivity function, which is presented in this article, is a novel diffusion coefficient that is more effective at removing noise and preserving edges than previous approaches. Finally, the performance of the proposed method is evaluated using the peak signal‐to‐noise ratio, mean square error, structural similarity index, and comparisons to other traditional methods. |
| Author | Muchahary, Deboraj Srinivasa Rao, Duggirala Ramalinga Reddy, Katta Kollem, Sreedhar Malathy, V. Ajayan, J. Rajendra Prasad, Chintha |
| Author_xml | – sequence: 1 givenname: Sreedhar orcidid: 0000-0002-9203-0404 surname: Kollem fullname: Kollem, Sreedhar email: ksreedhar829@gmail.com organization: SR University – sequence: 2 givenname: Katta surname: Ramalinga Reddy fullname: Ramalinga Reddy, Katta organization: G. Narayanamma Institute of Technology and Science (GNITS) – sequence: 3 givenname: Duggirala surname: Srinivasa Rao fullname: Srinivasa Rao, Duggirala organization: JNTUH College of Engineering – sequence: 4 givenname: Chintha surname: Rajendra Prasad fullname: Rajendra Prasad, Chintha organization: SR University – sequence: 5 givenname: V. surname: Malathy fullname: Malathy, V. organization: SR University – sequence: 6 givenname: J. surname: Ajayan fullname: Ajayan, J. organization: SR University – sequence: 7 givenname: Deboraj surname: Muchahary fullname: Muchahary, Deboraj organization: National Institute of Technology |
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| SubjectTerms | adaptive Bayesian threshold Algorithms Diffusion coefficient Diffusivity fourth‐order partial differential equation improved generalized cross‐validation Magnetic resonance imaging Medical imaging new diffusivity function Noise reduction Partial differential equations Poisson noise quaternion wavelet transform Quaternions Wavelet transforms |
| Title | Image denoising for magnetic resonance imaging medical images using improved generalized cross‐validation based on the diffusivity function |
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